In this notebook resides all of my code for analizing our sequences in terms of their ARE score distributions... the first code block will append our ARE score information and boolian tissue presence indicators for use in upset plotting and ARE expression differential analysis.
In [3]:
import pandas as pd
import os
# Function to read sequences from a FASTA file into a dictionary
def read_fasta(file_path):
sequences = {}
with open(file_path, 'r') as f:
gene_id = ""
transcript_id = ""
sequence = ""
for line in f:
if line.startswith(">"):
if gene_id and sequence:
if gene_id not in sequences:
sequences[gene_id] = {}
sequences[gene_id][transcript_id] = sequence
header_parts = line.strip().lstrip(">").split('|')
gene_id = header_parts[0]
transcript_id = header_parts[1]
sequence = ""
else:
sequence += line.strip()
if gene_id and sequence:
if gene_id not in sequences:
sequences[gene_id] = {}
sequences[gene_id][transcript_id] = sequence
return sequences
def parse_are_score(file_path, longest_transcripts, sequences):
print(f"Parsing ARE score data from {file_path}...")
with open(file_path, 'r') as f:
lines = f.readlines()[1:] # Skip header
for line in lines:
parts = line.strip().split('\t')
gene_id = parts[0].split('|')[0]
transcript_id = parts[0].split('|')[1]
score = float(parts[1])
pentamer_count = int(parts[2])
pentamer_position = parts[3]
au_block_position = parts[4]
sequence_length = int(parts[5])
sequence = ""
if gene_id in sequences and transcript_id in sequences[gene_id]:
sequence = sequences[gene_id][transcript_id]
else:
print(f"Warning: No sequence found for {transcript_id}")
# Check if we should update the longest transcript information for this gene
if gene_id not in longest_transcripts or sequence_length > longest_transcripts[gene_id]['Sequence_length']:
longest_transcripts[gene_id] = {
'ARE_score': score,
'Pentamer_count': pentamer_count,
'Pentamer_position': pentamer_position,
'AU_block_position': au_block_position,
'Sequence': sequence,
'Sequence_length': sequence_length
}
print(f"Updated longest transcript for {gene_id}: {sequence_length}")
print(f"Completed parsing {file_path}. Total genes: {len(longest_transcripts)}")
return longest_transcripts
def count_motifs(lengths):
motif_7mer = "TATTTAT"
motif_5mer = "ATTTA"
motif_counts = {'7mer_count': {}, '5mer_count': {}}
for key, val in lengths.items():
i_count_7mer, i_count_5mer = 0, 0
sequence = val['Sequence']
for i in range(0, len(sequence) - len(motif_7mer) + 1):
if sequence[i:i + len(motif_7mer)] == motif_7mer:
i_count_7mer += 1
for i in range(0, len(sequence) - len(motif_5mer) + 1):
if sequence[i:i + len(motif_5mer)] == motif_5mer:
i_count_5mer += 1
motif_counts['7mer_count'][key] = i_count_7mer
motif_counts['5mer_count'][key] = i_count_5mer
return motif_counts
def update_annotated_files(input_dir, output_dir, regulation, are_score_data, motif_counts):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
files = [f for f in os.listdir(input_dir) if f.endswith("_annotated.csv")]
total_files = len(files)
print(f"Updating {total_files} `{regulation}` files in {input_dir}...")
gene_presence = {tissue: set() for tissue in ["BM", "Spleen", "Ovary", "Liver", "Lung", "Kidney"]}
# First pass: identify the presence of each gene in each tissue
for file in files:
input_file_path = os.path.join(input_dir, file)
df = pd.read_csv(input_file_path)
tissue = file.split('_')[0]
for gene_id in df['ID']:
gene_presence[tissue].add(gene_id)
# Update the files with ARE score data and Boolean indicators
for idx, file in enumerate(files):
input_file_path = os.path.join(input_dir, file)
df = pd.read_csv(input_file_path)
# Extract tissue type from filename
tissue = file.split('_')[0]
# Create new columns
df['ARE_score'] = df['ID'].map(lambda x: are_score_data.get(x, {}).get('ARE_score', 'N/A'))
df['Pentamer_count'] = df['ID'].map(lambda x: are_score_data.get(x, {}).get('Pentamer_count', 'N/A'))
df['Pentamer_position'] = df['ID'].map(lambda x: are_score_data.get(x, {}).get('Pentamer_position', 'N/A'))
df['AU_block_position'] = df['ID'].map(lambda x: are_score_data.get(x, {}).get('AU_block_position', 'N/A'))
df['Sequence_length'] = df['ID'].map(lambda x: are_score_data.get(x, {}).get('Sequence_length', 'N/A'))
df['5mer_count'] = df['ID'].map(lambda x: motif_counts['5mer_count'].get(x, 'N/A'))
df['7mer_count'] = df['ID'].map(lambda x: motif_counts['7mer_count'].get(x, 'N/A'))
# Add Boolean indicators for each tissue
df['BM'] = df['ID'].map(lambda x: 1 if x in gene_presence['BM'] else 0)
df['Spleen'] = df['ID'].map(lambda x: 1 if x in gene_presence['Spleen'] else 0)
df['Ovary'] = df['ID'].map(lambda x: 1 if x in gene_presence['Ovary'] else 0)
df['Liver'] = df['ID'].map(lambda x: 1 if x in gene_presence['Liver'] else 0)
df['Lung'] = df['ID'].map(lambda x: 1 if x in gene_presence['Lung'] else 0)
df['Kidney'] = df['ID'].map(lambda x: 1 if x in gene_presence['Kidney'] else 0)
# Save the updated file
output_file_path = os.path.join(output_dir, file.replace("_annotated.csv", "_annotated_updated.csv"))
df.to_csv(output_file_path, index=False)
# Print progress information
print(f"Processed {idx + 1}/{total_files} files. Current file: {file}")
print(f"Completed updating files in {input_dir}.")
# Read sequences from the FASTA files
print("Reading sequences from FASTA files...")
sequences_38 = read_fasta('/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE/sequences/GRC_38p6_full.fasta')
# sequences_39 = read_fasta('/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE/sequences/GRC39_full.fasta')
# Combine sequences dictionaries, assuming unique transcript IDs across the versions
all_sequences = {**sequences_38}
# Initialize the dictionary for ARE score data
are_score_data = {}
# Parse ARE score data from both the GRC38 and GRC39 files
print("Parsing ARE score data from txt files...")
are_score_data = parse_are_score('ARE_score_data/3primeUTR_AREscore_GRC38p6_full.txt', are_score_data, all_sequences)
are_score_data = parse_are_score('ARE_score_data/3primeUTR_AREscore_GRC39_full.txt', are_score_data, all_sequences)
# Calculate motif counts
motif_counts = count_motifs(are_score_data)
print("Completed parsing ARE score data and calculating motif counts.")
# Update the upregulated and downregulated files
update_annotated_files("upregulated_genes", "upregulated_genes_updated", "upregulated", are_score_data, motif_counts)
update_annotated_files("unregulated_genes", "unregulated_genes_updated", "unregulated", are_score_data, motif_counts)
update_annotated_files("downregulated_genes", "downregulated_genes_updated", "downregulated", are_score_data, motif_counts)
print("All files have been updated successfully.")
Reading sequences from FASTA files... Parsing ARE score data from txt files... Parsing ARE score data from ARE_score_data/3primeUTR_AREscore_GRC38p6_full.txt... Updated longest transcript for ENSMUSG00000031816: 305 Updated longest transcript for ENSMUSG00000049252: 2520 Updated longest transcript for ENSMUSG00000020069: 974 Updated longest transcript for ENSMUSG00000022940: 28 Updated longest transcript for ENSMUSG00000043510: 321 Updated longest transcript for ENSMUSG00000031399: 78 Updated longest transcript for ENSMUSG00000042717: 3667 Updated longest transcript for ENSMUSG00000020721: 945 Updated longest transcript for ENSMUSG00000022157: 652 Updated longest transcript for ENSMUSG00000052387: 24 Updated longest transcript for ENSMUSG00000111647: 671 Updated longest transcript for ENSMUSG00000037646: 1129 Updated longest transcript for ENSMUSG00000021090: 564 Updated longest transcript for ENSMUSG00000008496: 233 Updated longest transcript for ENSMUSG00000052632: 2379 Updated longest transcript for ENSMUSG00000034544: 1970 Updated longest transcript for ENSMUSG00000073684: 480 Updated longest transcript for ENSMUSG00000041769: 314 Updated longest transcript for ENSMUSG00000026064: 2904 Updated longest transcript for ENSMUSG00000031358: 752 Updated longest transcript for ENSMUSG00000022024: 6243 Updated longest transcript for ENSMUSG00000027523: 356 Updated longest transcript for ENSMUSG00000096044: 437 Updated longest transcript for ENSMUSG00000017969: 12 Updated longest transcript for ENSMUSG00000025175: 3193 Updated longest transcript for ENSMUSG00000021733: 151 Updated longest transcript for ENSMUSG00000039197: 624 Updated longest transcript for ENSMUSG00000059994: 449 Updated longest transcript for ENSMUSG00000025499: 678 Updated longest transcript for ENSMUSG00000042699: 311 Updated longest transcript for ENSMUSG00000046337: 378 Updated longest transcript for ENSMUSG00000036257: 493 Updated longest transcript for ENSMUSG00000075229: 227 Updated longest transcript for ENSMUSG00000003865: 480 Updated longest transcript for ENSMUSG00000030979: 614 Updated longest transcript for ENSMUSG00000101155: 195 Updated longest transcript for ENSMUSG00000028876: 689 Updated longest transcript for ENSMUSG00000022556: 621 Updated longest transcript for ENSMUSG00000091140: 738 Updated longest transcript for ENSMUSG00000057540: 302 Updated longest transcript for ENSMUSG00000036291: 1840 Updated longest transcript for ENSMUSG00000022528: 368 Updated longest transcript for ENSMUSG00000074886: 1088 Updated longest transcript for ENSMUSG00000031539: 1999 Updated longest transcript for ENSMUSG00000034647: 97 Updated longest transcript for ENSMUSG00000000538: 3378 Updated longest transcript for ENSMUSG00000095765: 1943 Updated longest transcript for ENSMUSG00000051166: 3545 Updated longest transcript for ENSMUSG00000024392: 83 Updated longest transcript for ENSMUSG00000043747: 1934 Updated longest transcript for ENSMUSG00000025056: 352 Updated longest transcript for ENSMUSG00000034037: 1398 Updated longest transcript for ENSMUSG00000041238: 3224 Updated longest transcript for ENSMUSG00000006007: 403 Updated longest transcript for ENSMUSG00000022433: 197 Updated longest transcript for ENSMUSG00000025728: 1177 Updated longest transcript for ENSMUSG00000091896: 1562 Updated longest transcript for ENSMUSG00000028114: 1089 Updated longest transcript for ENSMUSG00000068742: 368 Updated longest transcript for ENSMUSG00000025665: 1587 Updated longest transcript for ENSMUSG00000074311: 147 Updated longest transcript for ENSMUSG00000027523: 1160 Updated longest transcript for ENSMUSG00000020262: 4056 Updated longest transcript for ENSMUSG00000029718: 60 Updated longest transcript for ENSMUSG00000038371: 1385 Updated longest transcript for ENSMUSG00000024524: 2074 Updated longest transcript for ENSMUSG00000048482: 2855 Updated longest transcript for ENSMUSG00000033849: 7218 Updated longest transcript for ENSMUSG00000052544: 7139 Updated longest transcript for ENSMUSG00000024404: 358 Updated longest transcript for ENSMUSG00000031513: 2150 Updated longest transcript for ENSMUSG00000022064: 282 Updated longest transcript for ENSMUSG00000051154: 304 Updated longest transcript for ENSMUSG00000031558: 1270 Updated longest transcript for ENSMUSG00000009741: 1816 Updated longest transcript for ENSMUSG00000023175: 338 Updated longest transcript for ENSMUSG00000021079: 197 Updated longest transcript for ENSMUSG00000052182: 5070 Updated longest transcript for ENSMUSG00000040728: 488 Updated longest transcript for ENSMUSG00000020827: 416 Updated longest transcript for ENSMUSG00000001909: 21 Updated longest transcript for ENSMUSG00000037410: 445 Updated longest transcript for ENSMUSG00000026981: 1306 Updated longest transcript for ENSMUSG00000038690: 130 Updated longest transcript for ENSMUSG00000024588: 1849 Updated longest transcript for ENSMUSG00000068270: 3731 Updated longest transcript for ENSMUSG00000040549: 438 Updated longest transcript for ENSMUSG00000028931: 1296 Updated longest transcript for ENSMUSG00000029472: 287 Updated longest transcript for ENSMUSG00000033728: 444 Updated longest transcript for ENSMUSG00000070837: 146 Updated longest transcript for ENSMUSG00000059149: 953 Updated longest transcript for ENSMUSG00000054087: 279 Updated longest transcript for ENSMUSG00000041669: 341 Updated longest transcript for ENSMUSG00000038587: 1001 Updated longest transcript for ENSMUSG00000022469: 592 Updated longest transcript for ENSMUSG00000015950: 1490 Updated longest transcript for ENSMUSG00000039242: 272 Updated longest transcript for ENSMUSG00000040025: 2012 Updated longest transcript for ENSMUSG00000026820: 354 Updated longest transcript for ENSMUSG00000057346: 444 Updated longest transcript for ENSMUSG00000022761: 540 Updated longest transcript for ENSMUSG00000027954: 752 Updated longest transcript for ENSMUSG00000026659: 1399 Updated longest transcript for ENSMUSG00000051682: 867 Updated longest transcript for ENSMUSG00000033020: 68 Updated longest transcript for ENSMUSG00000030304: 32 Updated longest transcript for ENSMUSG00000006764: 1026 Updated longest transcript for ENSMUSG00000027525: 970 Updated longest transcript for ENSMUSG00000035478: 485 Updated longest transcript for ENSMUSG00000032598: 125 Updated longest transcript for ENSMUSG00000028048: 57 Updated longest transcript for ENSMUSG00000075085: 1010 Updated longest transcript for ENSMUSG00000038065: 40 Updated longest transcript for ENSMUSG00000044737: 517 Updated longest transcript for ENSMUSG00000000131: 722 Updated longest transcript for ENSMUSG00000094734: 261 Updated longest transcript for ENSMUSG00000028101: 955 Updated longest transcript for ENSMUSG00000022962: 466 Updated longest transcript for ENSMUSG00000021997: 443 Updated longest transcript for ENSMUSG00000029260: 1435 Updated longest transcript for ENSMUSG00000030766: 900 Updated longest transcript for ENSMUSG00000001021: 347 Updated longest transcript for ENSMUSG00000028523: 183 Updated longest transcript for ENSMUSG00000075330: 464 Updated longest transcript for ENSMUSG00000072259: 20 Updated longest transcript for ENSMUSG00000047423: 606 Updated longest transcript for ENSMUSG00000029919: 2631 Updated longest transcript for ENSMUSG00000040086: 428 Updated longest transcript for ENSMUSG00000117863: 7627 Updated longest transcript for ENSMUSG00000103243: 204 Updated longest transcript for ENSMUSG00000042331: 452 Updated longest transcript for ENSMUSG00000095322: 549 Updated longest transcript for ENSMUSG00000015522: 178 Updated longest transcript for ENSMUSG00000063077: 1905 Updated longest transcript for ENSMUSG00000030232: 1054 Updated longest transcript for ENSMUSG00000049103: 2701 Updated longest transcript for ENSMUSG00000030254: 832 Updated longest transcript for ENSMUSG00000068250: 180 Updated longest transcript for ENSMUSG00000039349: 2382 Updated longest transcript for ENSMUSG00000030720: 867 Updated longest transcript for ENSMUSG00000017615: 2455 Updated longest transcript for ENSMUSG00000022021: 106 Updated longest transcript for ENSMUSG00000034285: 1373 Updated longest transcript for ENSMUSG00000032028: 1043 Updated longest transcript for ENSMUSG00000025920: 2612 Updated longest transcript for ENSMUSG00000020389: 1238 Updated longest transcript for ENSMUSG00000033439: 319 Updated longest transcript for ENSMUSG00000039201: 233 Updated longest transcript for ENSMUSG00000038264: 1264 Updated longest transcript for ENSMUSG00000091779: 28 Updated longest transcript for ENSMUSG00000004562: 288 Updated longest transcript for ENSMUSG00000084786: 110 Updated longest transcript for ENSMUSG00000041842: 2269 Updated longest transcript for ENSMUSG00000044098: 2043 Updated longest transcript for ENSMUSG00000047180: 281 Updated longest transcript for ENSMUSG00000039536: 144 Updated longest transcript for ENSMUSG00000042210: 168 Updated longest transcript for ENSMUSG00000030946: 478 Updated longest transcript for ENSMUSG00000027381: 2899 Updated longest transcript for ENSMUSG00000011486: 223 Updated longest transcript for ENSMUSG00000031883: 664 Updated longest transcript for ENSMUSG00000044222: 228 Updated longest transcript for ENSMUSG00000038777: 736 Updated longest transcript for ENSMUSG00000030804: 1678 Updated longest transcript for ENSMUSG00000034930: 359 Updated longest transcript for ENSMUSG00000027957: 475 Updated longest transcript for ENSMUSG00000026020: 159 Updated longest transcript for ENSMUSG00000097333: 655 Updated longest transcript for ENSMUSG00000029883: 555 Updated longest transcript for ENSMUSG00000029823: 185 Updated longest transcript for ENSMUSG00000029638: 4296 Updated longest transcript for ENSMUSG00000015316: 1561 Updated longest transcript for ENSMUSG00000073888: 31 Updated longest transcript for ENSMUSG00000048277: 379 Updated longest transcript for ENSMUSG00000019785: 2935 Updated longest transcript for ENSMUSG00000018907: 261 Updated longest transcript for ENSMUSG00000059887: 1230 Updated longest transcript for ENSMUSG00000002871: 284 Updated longest transcript for ENSMUSG00000075592: 1776 Updated longest transcript for ENSMUSG00000063698: 225 Updated longest transcript for ENSMUSG00000031078: 1230 Updated longest transcript for ENSMUSG00000034891: 649 Updated longest transcript for ENSMUSG00000032690: 1625 Updated longest transcript for ENSMUSG00000053192: 146 Updated longest transcript for ENSMUSG00000040888: 1090 Updated longest transcript for ENSMUSG00000029505: 1196 Updated longest transcript for ENSMUSG00000024248: 685 Updated longest transcript for ENSMUSG00000111611: 780 Updated longest transcript for ENSMUSG00000022090: 398 Updated longest transcript for ENSMUSG00000032594: 237 Updated longest transcript for ENSMUSG00000009470: 5637 Updated longest transcript for ENSMUSG00000034032: 1333 Updated longest transcript for ENSMUSG00000006784: 1045 Updated longest transcript for ENSMUSG00000020787: 1074 Updated longest transcript for ENSMUSG00000037904: 93 Updated longest transcript for ENSMUSG00000042992: 84 Updated longest transcript for ENSMUSG00000041654: 1564 Updated longest transcript for ENSMUSG00000109324: 76 Updated longest transcript for ENSMUSG00000045409: 2791 Updated longest transcript for ENSMUSG00000021477: 109 Updated longest transcript for ENSMUSG00000078198: 643 Updated longest transcript for ENSMUSG00000060176: 2880 Updated longest transcript for ENSMUSG00000028479: 3043 Updated longest transcript for ENSMUSG00000045078: 1756 Updated longest transcript for ENSMUSG00000029422: 751 Updated longest transcript for ENSMUSG00000029697: 693 Updated longest transcript for ENSMUSG00000055109: 219 Updated longest transcript for ENSMUSG00000030428: 2880 Updated longest transcript for ENSMUSG00000020884: 251 Updated longest transcript for ENSMUSG00000059610: 527 Updated longest transcript for ENSMUSG00000025372: 1517 Updated longest transcript for ENSMUSG00000020102: 525 Updated longest transcript for ENSMUSG00000008226: 1265 Updated longest transcript for ENSMUSG00000018565: 202 Updated longest transcript for ENSMUSG00000000325: 405 Updated longest transcript for ENSMUSG00000099655: 594 Updated longest transcript for ENSMUSG00000038172: 135 Updated longest transcript for ENSMUSG00000045095: 3491 Updated longest transcript for ENSMUSG00000024773: 458 Updated longest transcript for ENSMUSG00000035152: 640 Updated longest transcript for ENSMUSG00000039174: 24 Updated longest transcript for ENSMUSG00000033985: 930 Updated longest transcript for ENSMUSG00000059206: 3293 Updated longest transcript for ENSMUSG00000025353: 978 Updated longest transcript for ENSMUSG00000035649: 354 Updated longest transcript for ENSMUSG00000095654: 74 Updated longest transcript for ENSMUSG00000022010: 365 Updated longest transcript for ENSMUSG00000027202: 1140 Updated longest transcript for ENSMUSG00000079144: 2699 Updated longest transcript for ENSMUSG00000062175: 1920 Updated longest transcript for ENSMUSG00000045126: 28 Updated longest transcript for ENSMUSG00000021007: 144 Updated longest transcript for ENSMUSG00000035126: 181 Updated longest transcript for ENSMUSG00000038515: 287 Updated longest transcript for ENSMUSG00000052419: 2797 Updated longest transcript for ENSMUSG00000044024: 399 Updated longest transcript for ENSMUSG00000026104: 2568 Updated longest transcript for ENSMUSG00000057337: 4073 Updated longest transcript for ENSMUSG00000031157: 192 Updated longest transcript for ENSMUSG00000027397: 764 Updated longest transcript for ENSMUSG00000037171: 702 Updated longest transcript for ENSMUSG00000094586: 431 Updated longest transcript for ENSMUSG00000052516: 1474 Updated longest transcript for ENSMUSG00000047123: 576 Updated longest transcript for ENSMUSG00000067928: 1790 Updated longest transcript for ENSMUSG00000107055: 125 Updated longest transcript for ENSMUSG00000054497: 69 Updated longest transcript for ENSMUSG00000010080: 1552 Updated longest transcript for ENSMUSG00000027569: 146 Updated longest transcript for ENSMUSG00000003644: 227 Updated longest transcript for ENSMUSG00000066867: 170 Updated longest transcript for ENSMUSG00000062783: 1591 Updated longest transcript for ENSMUSG00000045534: 804 Updated longest transcript for ENSMUSG00000003062: 381 Updated longest transcript for ENSMUSG00000079563: 96 Updated longest transcript for ENSMUSG00000036564: 558 Updated longest transcript for ENSMUSG00000014633: 535 Updated longest transcript for ENSMUSG00000024889: 412 Updated longest transcript for ENSMUSG00000030617: 46 Updated longest transcript for ENSMUSG00000036371: 386 Updated longest transcript for ENSMUSG00000053205: 1320 Updated longest transcript for ENSMUSG00000025510: 776 Updated longest transcript for ENSMUSG00000024357: 337 Updated longest transcript for ENSMUSG00000074300: 947 Updated longest transcript for ENSMUSG00000092048: 4197 Updated longest transcript for ENSMUSG00000005936: 2474 Updated longest transcript for ENSMUSG00000029630: 411 Updated longest transcript for ENSMUSG00000038656: 132 Updated longest transcript for ENSMUSG00000000244: 747 Updated longest transcript for ENSMUSG00000026480: 876 Updated longest transcript for ENSMUSG00000021910: 638 Updated longest transcript for ENSMUSG00000039285: 532 Updated longest transcript for ENSMUSG00000025195: 126 Updated longest transcript for ENSMUSG00000021027: 1393 Updated longest transcript for ENSMUSG00000006519: 102 Updated longest transcript for ENSMUSG00000037820: 214 Updated longest transcript for ENSMUSG00000067360: 2024 Updated longest transcript for ENSMUSG00000039252: 4446 Updated longest transcript for ENSMUSG00000038671: 374 Updated longest transcript for ENSMUSG00000112858: 457 Updated longest transcript for ENSMUSG00000058014: 95 Updated longest transcript for ENSMUSG00000015947: 449 Updated longest transcript for ENSMUSG00000035898: 3363 Updated longest transcript for ENSMUSG00000095664: 10529 Updated longest transcript for ENSMUSG00000056598: 103 Updated longest transcript for ENSMUSG00000052922: 261 Updated longest transcript for ENSMUSG00000036002: 1121 Updated longest transcript for ENSMUSG00000031118: 258 Updated longest transcript for ENSMUSG00000009569: 209 Updated longest transcript for ENSMUSG00000029017: 108 Updated longest transcript for ENSMUSG00000024424: 259 Updated longest transcript for ENSMUSG00000032011: 285 Updated longest transcript for ENSMUSG00000021958: 231 Updated longest transcript for ENSMUSG00000100249: 361 Updated longest transcript for ENSMUSG00000045098: 80 Updated longest transcript for ENSMUSG00000064023: 53 Updated longest transcript for ENSMUSG00000021000: 423 Updated longest transcript for ENSMUSG00000045411: 785 Updated longest transcript for ENSMUSG00000022558: 345 Updated longest transcript for ENSMUSG00000030226: 1176 Updated longest transcript for ENSMUSG00000046110: 844 Updated longest transcript for ENSMUSG00000046688: 1253 Updated longest transcript for ENSMUSG00000052566: 1178 Updated longest transcript for ENSMUSG00000025077: 333 Updated longest transcript for ENSMUSG00000094612: 707 Updated longest transcript for ENSMUSG00000038658: 2153 Updated longest transcript for ENSMUSG00000038429: 528 Updated longest transcript for ENSMUSG00000044548: 1213 Updated longest transcript for ENSMUSG00000023919: 437 Updated longest transcript for ENSMUSG00000050945: 611 Updated longest transcript for ENSMUSG00000001666: 201 Updated longest transcript for ENSMUSG00000054746: 166 Updated longest transcript for ENSMUSG00000020056: 166 Updated longest transcript for ENSMUSG00000022501: 158 Updated longest transcript for ENSMUSG00000022967: 918 Updated longest transcript for ENSMUSG00000025967: 874 Updated longest transcript for ENSMUSG00000027513: 627 Updated longest transcript for ENSMUSG00000013736: 861 Updated longest transcript for ENSMUSG00000094690: 161 Updated longest transcript for ENSMUSG00000073002: 509 Updated longest transcript for ENSMUSG00000024603: 1498 Updated longest transcript for ENSMUSG00000049018: 1 Updated longest transcript for ENSMUSG00000000154: 53 Updated longest transcript for ENSMUSG00000062991: 1056 Updated longest transcript for ENSMUSG00000006586: 3741 Updated longest transcript for ENSMUSG00000062165: 118 Updated longest transcript for ENSMUSG00000028776: 346 Updated longest transcript for ENSMUSG00000044703: 436 Updated longest transcript for ENSMUSG00000031976: 1869 Updated longest transcript for ENSMUSG00000041907: 285 Updated longest transcript for ENSMUSG00000020439: 154 Updated longest transcript for ENSMUSG00000054237: 277 Updated longest transcript for ENSMUSG00000032826: 2345 Updated longest transcript for ENSMUSG00000030435: 277 Updated longest transcript for ENSMUSG00000068762: 380 Updated longest transcript for ENSMUSG00000022768: 169 Updated longest transcript for ENSMUSG00000027797: 4689 Updated longest transcript for ENSMUSG00000051906: 321 Updated longest transcript for ENSMUSG00000023150: 2225 Updated longest transcript for ENSMUSG00000045994: 2204 Updated longest transcript for ENSMUSG00000057604: 459 Updated longest transcript for 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Updated longest transcript for ENSMUSG00000020571: 764 Updated longest transcript for ENSMUSG00000016346: 5474 Updated longest transcript for ENSMUSG00000022596: 269 Updated longest transcript for ENSMUSG00000079592: 373 Updated longest transcript for ENSMUSG00000018819: 649 Updated longest transcript for ENSMUSG00000037531: 2197 Updated longest transcript for ENSMUSG00000090751: 7382 Updated longest transcript for ENSMUSG00000017466: 2630 Updated longest transcript for ENSMUSG00000045005: 4285 Updated longest transcript for ENSMUSG00000042097: 1383 Updated longest transcript for ENSMUSG00000031430: 1416 Updated longest transcript for ENSMUSG00000057855: 278 Updated longest transcript for ENSMUSG00000043702: 5567 Updated longest transcript for ENSMUSG00000028591: 930 Updated longest transcript for ENSMUSG00000021139: 200 Updated longest transcript for ENSMUSG00000023411: 1102 Updated longest transcript for ENSMUSG00000018844: 1129 Updated longest transcript for ENSMUSG00000013663: 6132 Updated longest transcript for ENSMUSG00000061878: 289 Updated longest transcript for ENSMUSG00000030994: 834 Updated longest transcript for ENSMUSG00000020171: 445 Updated longest transcript for ENSMUSG00000037278: 720 Updated longest transcript for ENSMUSG00000020694: 2692 Updated longest transcript for ENSMUSG00000037570: 308 Updated longest transcript for ENSMUSG00000020431: 8791 Updated longest transcript for ENSMUSG00000024256: 2410 Updated longest transcript for ENSMUSG00000036353: 937 Updated longest transcript for ENSMUSG00000040026: 1956 Updated longest transcript for ENSMUSG00000057425: 267 Updated longest transcript for ENSMUSG00000068040: 1710 Updated longest transcript for ENSMUSG00000029335: 4467 Updated longest transcript for ENSMUSG00000021301: 4211 Updated longest transcript for ENSMUSG00000025473: 545 Updated longest transcript for ENSMUSG00000022033: 409 Updated longest transcript for ENSMUSG00000041028: 2050 Updated longest transcript for ENSMUSG00000053536: 2105 Updated longest transcript for ENSMUSG00000043719: 150 Updated longest transcript for ENSMUSG00000026109: 271 Updated longest transcript for ENSMUSG00000067279: 1684 Updated longest transcript for ENSMUSG00000103092: 2401 Updated longest transcript for ENSMUSG00000049115: 834 Updated longest transcript for ENSMUSG00000022039: 332 Updated longest transcript for ENSMUSG00000048304: 1230 Updated longest transcript for ENSMUSG00000036744: 1674 Updated longest transcript for ENSMUSG00000027692: 5995 Updated longest transcript for ENSMUSG00000046637: 1344 Updated longest transcript for ENSMUSG00000052749: 3401 Updated longest transcript for ENSMUSG00000053054: 1197 Updated longest transcript for ENSMUSG00000027831: 3547 Updated longest transcript for ENSMUSG00000001964: 739 Updated longest transcript for ENSMUSG00000026360: 2342 Updated longest transcript for ENSMUSG00000020100: 3764 Updated longest transcript for ENSMUSG00000023330: 302 Updated longest transcript for ENSMUSG00000032496: 585 Updated longest transcript for ENSMUSG00000021548: 646 Updated longest transcript for ENSMUSG00000059714: 691 Updated longest transcript for ENSMUSG00000072949: 164 Updated longest transcript for ENSMUSG00000037938: 836 Updated longest transcript for ENSMUSG00000031585: 604 Updated longest transcript for ENSMUSG00000052688: 3742 Updated longest transcript for ENSMUSG00000018893: 435 Updated longest transcript for ENSMUSG00000037286: 3423 Updated longest transcript for ENSMUSG00000089989: 24 Updated longest transcript for ENSMUSG00000021460: 1201 Updated longest transcript for ENSMUSG00000047443: 1741 Updated longest transcript for ENSMUSG00000058331: 370 Updated longest transcript for ENSMUSG00000029458: 3079 Updated longest transcript for ENSMUSG00000049300: 1892 Updated longest transcript for ENSMUSG00000025010: 1911 Updated longest transcript for ENSMUSG00000028967: 1390 Updated longest transcript for ENSMUSG00000037593: 248 Updated longest transcript for ENSMUSG00000020459: 194 Updated longest transcript for ENSMUSG00000035177: 317 Updated longest transcript for ENSMUSG00000021235: 713 Updated longest transcript for ENSMUSG00000030549: 462 Updated longest transcript for ENSMUSG00000021539: 377 Updated longest transcript for ENSMUSG00000025381: 492 Updated longest transcript for ENSMUSG00000041193: 1296 Updated longest transcript for ENSMUSG00000074437: 112 Updated longest transcript for ENSMUSG00000018387: 671 Updated longest transcript for ENSMUSG00000039568: 741 Updated longest transcript for ENSMUSG00000057173: 162 Updated longest transcript for ENSMUSG00000027983: 2688 Updated longest transcript for ENSMUSG00000054309: 1168 Updated longest transcript for ENSMUSG00000039967: 1848 Updated longest transcript for ENSMUSG00000074139: 461 Updated longest transcript for ENSMUSG00000060475: 1740 Updated longest transcript for ENSMUSG00000026600: 6534 Updated longest transcript for ENSMUSG00000063810: 115 Updated longest transcript for ENSMUSG00000001420: 673 Updated longest transcript for ENSMUSG00000031965: 449 Updated longest transcript for ENSMUSG00000050395: 4986 Updated longest transcript for ENSMUSG00000054626: 284 Updated longest transcript for ENSMUSG00000035314: 1764 Updated longest transcript for ENSMUSG00000032079: 359 Updated longest transcript for ENSMUSG00000035242: 286 Updated longest transcript for ENSMUSG00000118459: 367 Updated longest transcript for ENSMUSG00000024610: 490 Updated longest transcript for ENSMUSG00000021428: 1672 Updated longest transcript for ENSMUSG00000028884: 878 Updated longest transcript for ENSMUSG00000014074: 2108 Updated longest transcript for ENSMUSG00000029599: 1300 Updated longest transcript for ENSMUSG00000025888: 330 Updated longest transcript for ENSMUSG00000059657: 81 Updated longest transcript for ENSMUSG00000037253: 1779 Updated longest transcript for ENSMUSG00000037257: 4304 Updated longest transcript for ENSMUSG00000038015: 33 Updated longest transcript for ENSMUSG00000026188: 1798 Updated longest transcript for ENSMUSG00000010307: 925 Updated longest transcript for ENSMUSG00000010651: 315 Updated longest transcript for ENSMUSG00000039174: 310 Updated longest transcript for ENSMUSG00000027309: 3000 Updated longest transcript for ENSMUSG00000054405: 603 Updated longest transcript for ENSMUSG00000029348: 1398 Updated longest transcript for ENSMUSG00000023977: 2751 Updated longest transcript for ENSMUSG00000012429: 2063 Updated longest transcript for ENSMUSG00000021916: 316 Updated longest transcript for ENSMUSG00000090213: 1453 Updated longest transcript for ENSMUSG00000022761: 3092 Updated longest transcript for ENSMUSG00000029564: 51 Updated longest transcript for ENSMUSG00000031691: 2053 Updated longest transcript for ENSMUSG00000032872: 892 Updated longest transcript for ENSMUSG00000027722: 358 Updated longest transcript for ENSMUSG00000048249: 5228 Updated longest transcript for ENSMUSG00000054666: 5968 Updated longest transcript for ENSMUSG00000074671: 1942 Updated longest transcript for ENSMUSG00000031403: 1016 Updated longest transcript for ENSMUSG00000075520: 213 Updated longest transcript for ENSMUSG00000056310: 723 Updated longest transcript for ENSMUSG00000055862: 129 Updated longest transcript for ENSMUSG00000058014: 2179 Updated longest transcript for ENSMUSG00000020717: 926 Updated longest transcript for ENSMUSG00000020120: 2944 Updated longest transcript for ENSMUSG00000019590: 567 Updated longest transcript for ENSMUSG00000061778: 874 Updated longest transcript for ENSMUSG00000022218: 194 Updated longest transcript for ENSMUSG00000042801: 2533 Updated longest transcript for ENSMUSG00000029149: 104 Updated longest transcript for ENSMUSG00000025571: 2586 Updated longest transcript for ENSMUSG00000023083: 54 Updated longest transcript for ENSMUSG00000025432: 431 Updated longest transcript for ENSMUSG00000052997: 597 Updated longest transcript for ENSMUSG00000094076: 3130 Updated longest transcript for ENSMUSG00000039476: 292 Updated longest transcript for ENSMUSG00000018774: 158 Updated longest transcript for ENSMUSG00000022899: 140 Updated longest transcript for ENSMUSG00000047250: 921 Updated longest transcript for ENSMUSG00000012117: 1989 Updated longest transcript for ENSMUSG00000078771: 551 Updated longest transcript for ENSMUSG00000051703: 721 Updated longest transcript for ENSMUSG00000020697: 2684 Updated longest transcript for ENSMUSG00000023999: 1054 Updated longest transcript for ENSMUSG00000023079: 405 Updated longest transcript for ENSMUSG00000059343: 727 Updated longest transcript for ENSMUSG00000078886: 79 Updated longest transcript for ENSMUSG00000026526: 413 Updated longest transcript for ENSMUSG00000051036: 617 Updated longest transcript for ENSMUSG00000057143: 2021 Updated longest transcript for ENSMUSG00000020234: 899 Updated longest transcript for ENSMUSG00000029560: 1226 Updated longest transcript for ENSMUSG00000024125: 160 Updated longest transcript for ENSMUSG00000015702: 694 Updated longest transcript for ENSMUSG00000022758: 2440 Updated longest transcript for ENSMUSG00000002617: 1352 Updated longest transcript for ENSMUSG00000025754: 1255 Updated longest transcript for ENSMUSG00000020591: 317 Updated longest transcript for ENSMUSG00000009394: 2235 Updated longest transcript for ENSMUSG00000094043: 301 Updated longest transcript for ENSMUSG00000029201: 735 Updated longest transcript for ENSMUSG00000000759: 2351 Updated longest transcript for ENSMUSG00000026401: 71 Updated longest transcript for ENSMUSG00000001376: 752 Updated longest transcript for ENSMUSG00000090137: 69 Updated longest transcript for ENSMUSG00000036067: 551 Updated longest transcript for ENSMUSG00000017831: 1251 Updated longest transcript for ENSMUSG00000038459: 1226 Updated longest transcript for ENSMUSG00000028179: 571 Updated longest transcript for ENSMUSG00000063334: 3751 Updated longest transcript for ENSMUSG00000020457: 359 Updated longest transcript for ENSMUSG00000043085: 1438 Updated longest transcript for ENSMUSG00000024498: 1074 Updated longest transcript for ENSMUSG00000031367: 2587 Updated longest transcript for ENSMUSG00000103897: 1744 Updated longest transcript for ENSMUSG00000020440: 444 Updated longest transcript for ENSMUSG00000042659: 116 Updated longest transcript for ENSMUSG00000036602: 913 Updated longest transcript for ENSMUSG00000048745: 2985 Updated longest transcript for ENSMUSG00000005897: 1860 Updated longest transcript for ENSMUSG00000034211: 4401 Updated longest transcript for ENSMUSG00000023802: 25 Updated longest transcript for ENSMUSG00000024143: 3009 Updated longest transcript for ENSMUSG00000025141: 1053 Updated longest transcript for ENSMUSG00000074870: 385 Updated longest transcript for ENSMUSG00000043583: 73 Updated longest transcript for ENSMUSG00000009670: 290 Updated longest transcript for ENSMUSG00000005886: 3656 Updated longest transcript for ENSMUSG00000037426: 3000 Updated longest transcript for ENSMUSG00000033306: 1709 Updated longest transcript for ENSMUSG00000030064: 1493 Updated longest transcript for ENSMUSG00000035713: 952 Updated longest transcript for ENSMUSG00000040325: 1000 Updated longest transcript for ENSMUSG00000059363: 631 Updated longest transcript for ENSMUSG00000032411: 1113 Updated longest transcript for ENSMUSG00000060556: 2408 Updated longest transcript for ENSMUSG00000038980: 297 Updated longest transcript for ENSMUSG00000048602: 140 Updated longest transcript for ENSMUSG00000037787: 2707 Updated longest transcript for ENSMUSG00000047098: 79 Updated longest transcript for ENSMUSG00000040225: 2859 Updated longest transcript for ENSMUSG00000032315: 942 Updated longest transcript for ENSMUSG00000021175: 1221 Updated longest transcript for ENSMUSG00000019838: 1143 Updated longest transcript for ENSMUSG00000026585: 1405 Updated longest transcript for ENSMUSG00000031323: 590 Updated longest transcript for ENSMUSG00000047343: 231 Updated longest transcript for ENSMUSG00000040717: 5900 Updated longest transcript for ENSMUSG00000059900: 524 Updated longest transcript for ENSMUSG00000030217: 1124 Updated longest transcript for ENSMUSG00000032212: 1284 Updated longest transcript for ENSMUSG00000059375: 1108 Updated longest transcript for ENSMUSG00000024561: 2516 Updated longest transcript for ENSMUSG00000041324: 82 Updated longest transcript for ENSMUSG00000024132: 233 Updated longest transcript for ENSMUSG00000023267: 280 Updated longest transcript for ENSMUSG00000094898: 3181 Updated longest transcript for ENSMUSG00000024960: 849 Updated longest transcript for ENSMUSG00000022120: 891 Updated longest transcript for ENSMUSG00000047773: 1385 Updated longest transcript for ENSMUSG00000025940: 770 Updated longest transcript for ENSMUSG00000030216: 554 Updated longest transcript for ENSMUSG00000024687: 2070 Updated longest transcript for ENSMUSG00000078118: 1686 Updated longest transcript for ENSMUSG00000030556: 2476 Updated longest transcript for ENSMUSG00000020875: 1839 Updated longest transcript for ENSMUSG00000020277: 1263 Updated longest transcript for ENSMUSG00000078664: 2568 Updated longest transcript for ENSMUSG00000075219: 444 Updated longest transcript for ENSMUSG00000049922: 1451 Updated longest transcript for ENSMUSG00000091077: 703 Updated longest transcript for ENSMUSG00000062410: 361 Updated longest transcript for ENSMUSG00000038886: 1104 Updated longest transcript for ENSMUSG00000062127: 2735 Updated longest transcript for ENSMUSG00000028991: 823 Updated longest transcript for ENSMUSG00000016529: 706 Updated longest transcript for ENSMUSG00000031441: 3820 Updated longest transcript for ENSMUSG00000007944: 1320 Updated longest transcript for ENSMUSG00000028391: 1145 Updated longest transcript for ENSMUSG00000034990: 166 Updated longest transcript for ENSMUSG00000021902: 746 Updated longest transcript for ENSMUSG00000037318: 1260 Updated longest transcript for ENSMUSG00000029188: 2034 Updated longest transcript for ENSMUSG00000026686: 1980 Updated longest transcript for ENSMUSG00000043999: 1010 Updated longest transcript for ENSMUSG00000035596: 1748 Updated longest transcript for ENSMUSG00000025736: 1252 Updated longest transcript for ENSMUSG00000055415: 2384 Updated longest transcript for ENSMUSG00000040009: 1695 Updated longest transcript for ENSMUSG00000025946: 434 Updated longest transcript for ENSMUSG00000050751: 1438 Updated longest transcript for ENSMUSG00000026213: 697 Updated longest transcript for ENSMUSG00000021604: 574 Updated longest transcript for ENSMUSG00000025318: 1342 Updated longest transcript for ENSMUSG00000023122: 365 Updated longest transcript for ENSMUSG00000095625: 110 Updated longest transcript for ENSMUSG00000039929: 458 Updated longest transcript for ENSMUSG00000001157: 1155 Updated longest transcript for ENSMUSG00000056290: 3956 Updated longest transcript for ENSMUSG00000027880: 1277 Updated longest transcript for ENSMUSG00000067201: 368 Updated longest transcript for ENSMUSG00000021943: 3470 Updated longest transcript for ENSMUSG00000070796: 733 Updated longest transcript for ENSMUSG00000020961: 6713 Updated longest transcript for ENSMUSG00000024738: 153 Updated longest transcript for ENSMUSG00000020644: 785 Updated longest transcript for ENSMUSG00000020142: 2086 Updated longest transcript for ENSMUSG00000062705: 367 Updated longest transcript for ENSMUSG00000062006: 412 Updated longest transcript for ENSMUSG00000001804: 218 Updated longest transcript for ENSMUSG00000008438: 213 Updated longest transcript for ENSMUSG00000031554: 276 Updated longest transcript for ENSMUSG00000059379: 1471 Updated longest transcript for ENSMUSG00000029658: 161 Updated longest transcript for ENSMUSG00000092060: 7878 Updated longest transcript for ENSMUSG00000028952: 546 Updated longest transcript for ENSMUSG00000020608: 2227 Updated longest transcript for ENSMUSG00000030894: 1821 Updated longest transcript for ENSMUSG00000046593: 2536 Updated longest transcript for ENSMUSG00000024393: 185 Updated longest transcript for ENSMUSG00000025894: 2322 Updated longest transcript for ENSMUSG00000023046: 209 Updated longest transcript for ENSMUSG00000000266: 3834 Updated longest transcript for ENSMUSG00000050312: 3236 Updated longest transcript for ENSMUSG00000018821: 416 Updated longest transcript for ENSMUSG00000020905: 958 Updated longest transcript for ENSMUSG00000037738: 470 Updated longest transcript for ENSMUSG00000013833: 576 Updated longest transcript for ENSMUSG00000030579: 173 Updated longest transcript for ENSMUSG00000114943: 731 Updated longest transcript for ENSMUSG00000023433: 75 Updated longest transcript for ENSMUSG00000001416: 217 Updated longest transcript for ENSMUSG00000035711: 187 Updated longest transcript for ENSMUSG00000029651: 519 Updated longest transcript for ENSMUSG00000046269: 954 Updated longest transcript for ENSMUSG00000023092: 1411 Updated longest transcript for ENSMUSG00000038151: 2490 Updated longest transcript for ENSMUSG00000045521: 213 Updated longest transcript for ENSMUSG00000040794: 160 Updated longest transcript for ENSMUSG00000022131: 514 Updated longest transcript for ENSMUSG00000023353: 444 Updated longest transcript for ENSMUSG00000086040: 2749 Updated longest transcript for ENSMUSG00000036907: 600 Updated longest transcript for ENSMUSG00000024277: 2930 Updated longest transcript for ENSMUSG00000040212: 43 Updated longest transcript for ENSMUSG00000044617: 3652 Updated longest transcript for ENSMUSG00000069299: 7112 Updated longest transcript for ENSMUSG00000063480: 1581 Updated longest transcript for ENSMUSG00000045780: 4642 Updated longest transcript for ENSMUSG00000093877: 100 Updated longest transcript for ENSMUSG00000038886: 2602 Updated longest transcript for ENSMUSG00000023942: 471 Updated longest transcript for ENSMUSG00000025858: 982 Updated longest transcript for ENSMUSG00000024240: 1284 Updated longest transcript for ENSMUSG00000028553: 63 Updated longest transcript for ENSMUSG00000031150: 1442 Updated longest transcript for ENSMUSG00000026023: 109 Updated longest transcript for ENSMUSG00000039323: 305 Updated longest transcript for ENSMUSG00000062937: 1575 Updated longest transcript for ENSMUSG00000015519: 291 Updated longest transcript for ENSMUSG00000027848: 4178 Updated longest transcript for ENSMUSG00000021506: 1018 Updated longest transcript for ENSMUSG00000015672: 2302 Updated longest transcript for ENSMUSG00000027857: 72 Updated longest transcript for ENSMUSG00000001508: 307 Updated longest transcript for ENSMUSG00000033852: 1418 Updated longest transcript for ENSMUSG00000032999: 588 Updated longest transcript for ENSMUSG00000033006: 1030 Updated longest transcript for ENSMUSG00000056209: 325 Updated longest transcript for ENSMUSG00000044364: 524 Updated longest transcript for ENSMUSG00000058446: 5069 Updated longest transcript for ENSMUSG00000041438: 107 Updated longest transcript for ENSMUSG00000015165: 1244 Updated longest transcript for ENSMUSG00000070305: 2653 Updated longest transcript for ENSMUSG00000070737: 624 Updated longest transcript for ENSMUSG00000040761: 1016 Updated longest transcript for ENSMUSG00000021987: 1849 Updated longest transcript for ENSMUSG00000061983: 28 Updated longest transcript for ENSMUSG00000036676: 3027 Updated longest transcript for ENSMUSG00000054958: 68 Updated longest transcript for ENSMUSG00000096703: 1917 Updated longest transcript for ENSMUSG00000021483: 2795 Updated longest transcript for ENSMUSG00000021136: 1845 Updated longest transcript for ENSMUSG00000033157: 1890 Updated longest transcript for ENSMUSG00000079114: 94 Updated longest transcript for ENSMUSG00000027425: 128 Updated longest transcript for ENSMUSG00000025403: 650 Updated longest transcript for ENSMUSG00000096666: 140 Updated longest transcript for ENSMUSG00000032375: 4011 Updated longest transcript for ENSMUSG00000040899: 73 Updated longest transcript for ENSMUSG00000000563: 1042 Updated longest transcript for ENSMUSG00000026646: 2808 Updated longest transcript for ENSMUSG00000030759: 3254 Updated longest transcript for ENSMUSG00000032783: 128 Updated longest transcript for ENSMUSG00000046678: 7050 Updated longest transcript for ENSMUSG00000026499: 1834 Updated longest transcript for ENSMUSG00000022425: 465 Updated longest transcript for ENSMUSG00000071770: 138 Updated longest transcript for ENSMUSG00000025041: 5501 Updated longest transcript for ENSMUSG00000034729: 396 Updated longest transcript for ENSMUSG00000028295: 325 Updated longest transcript for ENSMUSG00000058244: 34 Updated longest transcript for ENSMUSG00000078994: 1750 Updated longest transcript for ENSMUSG00000073951: 441 Updated longest transcript for ENSMUSG00000027206: 594 Updated longest transcript for ENSMUSG00000000159: 670 Updated longest transcript for ENSMUSG00000035632: 1531 Updated longest transcript for ENSMUSG00000000247: 604 Updated longest transcript for ENSMUSG00000030035: 626 Updated longest transcript for ENSMUSG00000034801: 1357 Updated longest transcript for ENSMUSG00000018656: 269 Updated longest transcript for ENSMUSG00000041920: 2289 Updated longest transcript for ENSMUSG00000049674: 3019 Updated longest transcript for ENSMUSG00000024769: 1079 Updated longest transcript for ENSMUSG00000045010: 560 Updated longest transcript for ENSMUSG00000035004: 1394 Updated longest transcript for ENSMUSG00000039356: 293 Updated longest transcript for ENSMUSG00000027584: 2593 Updated longest transcript for ENSMUSG00000033676: 4043 Updated longest transcript for ENSMUSG00000042251: 5351 Updated longest transcript for ENSMUSG00000106723: 672 Updated longest transcript for ENSMUSG00000006589: 704 Updated longest transcript for ENSMUSG00000067995: 597 Updated longest transcript for ENSMUSG00000096852: 27 Updated longest transcript for ENSMUSG00000037112: 2524 Updated longest transcript for ENSMUSG00000029992: 4039 Updated longest transcript for ENSMUSG00000003072: 319 Updated longest transcript for ENSMUSG00000030386: 3466 Updated longest transcript for ENSMUSG00000031727: 236 Updated longest transcript for ENSMUSG00000056564: 2390 Updated longest transcript for ENSMUSG00000003380: 169 Updated longest transcript for ENSMUSG00000045337: 50 Updated longest transcript for ENSMUSG00000040731: 1632 Updated longest transcript for ENSMUSG00000038375: 3058 Updated longest transcript for ENSMUSG00000072675: 955 Updated longest transcript for ENSMUSG00000046879: 552 Updated longest transcript for ENSMUSG00000089862: 1911 Updated longest transcript for ENSMUSG00000068011: 2176 Updated longest transcript for ENSMUSG00000098754: 2575 Updated longest transcript for ENSMUSG00000061666: 1306 Updated longest transcript for ENSMUSG00000038042: 1864 Updated longest transcript for ENSMUSG00000022249: 536 Updated longest transcript for ENSMUSG00000068037: 445 Updated longest transcript for ENSMUSG00000015189: 1237 Updated longest transcript for ENSMUSG00000024030: 3703 Updated longest transcript for ENSMUSG00000036615: 1350 Updated longest transcript for ENSMUSG00000061353: 5188 Updated longest transcript for ENSMUSG00000003847: 2379 Updated longest transcript for ENSMUSG00000025573: 205 Updated longest transcript for ENSMUSG00000062101: 172 Updated longest transcript for ENSMUSG00000052605: 108 Updated longest transcript for ENSMUSG00000020086: 632 Updated longest transcript for ENSMUSG00000001305: 425 Updated longest transcript for ENSMUSG00000058833: 142 Updated longest transcript for ENSMUSG00000021782: 1648 Updated longest transcript for ENSMUSG00000032329: 2410 Updated longest transcript for ENSMUSG00000065999: 962 Updated longest transcript for ENSMUSG00000068071: 240 Updated longest transcript for ENSMUSG00000051065: 1569 Updated longest transcript for ENSMUSG00000048620: 5362 Updated longest transcript for ENSMUSG00000095490: 767 Updated longest transcript for ENSMUSG00000018761: 506 Updated longest transcript for ENSMUSG00000057606: 1328 Updated longest transcript for ENSMUSG00000005364: 1757 Updated longest transcript for ENSMUSG00000035828: 964 Updated longest transcript for ENSMUSG00000045598: 993 Updated longest transcript for ENSMUSG00000002104: 208 Updated longest transcript for ENSMUSG00000066867: 698 Updated longest transcript for ENSMUSG00000040663: 1563 Updated longest transcript for ENSMUSG00000060126: 314 Updated longest transcript for ENSMUSG00000022370: 488 Updated longest transcript for ENSMUSG00000022602: 1668 Updated longest transcript for ENSMUSG00000044120: 2511 Updated longest transcript for ENSMUSG00000076434: 292 Updated longest transcript for ENSMUSG00000019297: 1702 Updated longest transcript for ENSMUSG00000027661: 1675 Updated longest transcript for ENSMUSG00000040482: 563 Updated longest transcript for ENSMUSG00000038069: 2942 Updated longest transcript for ENSMUSG00000034453: 1398 Updated longest transcript for ENSMUSG00000036390: 270 Updated longest transcript for ENSMUSG00000047613: 327 Updated longest transcript for ENSMUSG00000043897: 1154 Updated longest transcript for ENSMUSG00000021886: 541 Updated longest transcript for ENSMUSG00000039275: 2932 Updated longest transcript for ENSMUSG00000025255: 2710 Updated longest transcript for ENSMUSG00000036099: 8671 Updated longest transcript for ENSMUSG00000006527: 4873 Updated longest transcript for ENSMUSG00000041571: 347 Updated longest transcript for ENSMUSG00000040600: 306 Updated longest transcript for ENSMUSG00000036557: 20 Updated longest transcript for ENSMUSG00000017167: 1065 Updated longest transcript for ENSMUSG00000047866: 2366 Updated longest transcript for ENSMUSG00000030340: 1307 Updated longest transcript for ENSMUSG00000051721: 2016 Updated longest transcript for ENSMUSG00000041505: 178 Updated longest transcript for ENSMUSG00000048078: 4502 Updated longest transcript for ENSMUSG00000032987: 95 Updated longest transcript for ENSMUSG00000023467: 480 Updated longest transcript for ENSMUSG00000039691: 546 Updated longest transcript for ENSMUSG00000026568: 304 Updated longest transcript for ENSMUSG00000034875: 724 Updated longest transcript for ENSMUSG00000032177: 1904 Updated longest transcript for ENSMUSG00000074817: 2120 Updated longest transcript for ENSMUSG00000074483: 134 Updated longest transcript for ENSMUSG00000043991: 4408 Updated longest transcript for ENSMUSG00000021772: 3814 Updated longest transcript for ENSMUSG00000009687: 2411 Updated longest transcript for ENSMUSG00000025269: 2906 Updated longest transcript for ENSMUSG00000051557: 293 Updated longest transcript for ENSMUSG00000095789: 1494 Updated longest transcript for ENSMUSG00000037326: 1436 Updated longest transcript for ENSMUSG00000044313: 1943 Updated longest transcript for ENSMUSG00000039092: 860 Updated longest transcript for ENSMUSG00000028698: 3224 Updated longest transcript for ENSMUSG00000002944: 983 Updated longest transcript for ENSMUSG00000070687: 812 Updated longest transcript for ENSMUSG00000036244: 172 Updated longest transcript for ENSMUSG00000051391: 2586 Updated longest transcript for ENSMUSG00000098892: 951 Updated longest transcript for ENSMUSG00000100923: 1538 Updated longest transcript for ENSMUSG00000014554: 1474 Updated longest transcript for ENSMUSG00000043909: 3582 Updated longest transcript for ENSMUSG00000037621: 973 Updated longest transcript for ENSMUSG00000022102: 422 Updated longest transcript for ENSMUSG00000027329: 1422 Updated longest transcript for ENSMUSG00000024072: 1705 Updated longest transcript for ENSMUSG00000079608: 441 Updated longest transcript for ENSMUSG00000039087: 3003 Updated longest transcript for ENSMUSG00000028394: 1717 Updated longest transcript for ENSMUSG00000032537: 1746 Updated longest transcript for ENSMUSG00000045573: 308 Updated longest transcript for ENSMUSG00000090472: 13 Updated longest transcript for ENSMUSG00000071068: 517 Updated longest transcript for ENSMUSG00000026640: 4708 Updated longest transcript for ENSMUSG00000090713: 762 Updated longest transcript for ENSMUSG00000022391: 941 Updated longest transcript for ENSMUSG00000069769: 5310 Updated longest transcript for ENSMUSG00000015971: 405 Updated longest transcript for ENSMUSG00000041859: 488 Updated longest transcript for ENSMUSG00000031997: 176 Updated longest transcript for ENSMUSG00000028617: 217 Updated longest transcript for ENSMUSG00000070720: 515 Updated longest transcript for ENSMUSG00000062527: 2234 Updated longest transcript for ENSMUSG00000037234: 10469 Updated longest transcript for ENSMUSG00000057719: 2733 Updated longest transcript for ENSMUSG00000050493: 250 Updated longest transcript for ENSMUSG00000094634: 1133 Updated longest transcript for ENSMUSG00000043068: 787 Updated longest transcript for ENSMUSG00000043940: 267 Updated longest transcript for ENSMUSG00000052534: 538 Updated longest transcript for ENSMUSG00000070031: 355 Updated longest transcript for ENSMUSG00000025155: 218 Updated longest transcript for ENSMUSG00000074102: 3386 Updated longest transcript for ENSMUSG00000043987: 1300 Updated longest transcript for ENSMUSG00000038079: 547 Updated longest transcript for ENSMUSG00000056203: 32 Updated longest transcript for ENSMUSG00000015126: 137 Updated longest transcript for ENSMUSG00000038369: 512 Updated longest transcript for ENSMUSG00000031576: 137 Updated longest transcript for ENSMUSG00000118590: 13 Updated longest transcript for ENSMUSG00000039318: 2763 Updated longest transcript for ENSMUSG00000036402: 1497 Updated longest transcript for ENSMUSG00000041629: 1991 Updated longest transcript for ENSMUSG00000043692: 695 Updated longest transcript for ENSMUSG00000030243: 1344 Updated longest transcript for ENSMUSG00000081948: 4734 Updated longest transcript for ENSMUSG00000021696: 3002 Updated longest transcript for ENSMUSG00000061983: 329 Updated longest transcript for ENSMUSG00000039167: 1790 Updated longest transcript for ENSMUSG00000034997: 3097 Updated longest transcript for ENSMUSG00000033228: 542 Updated longest transcript for ENSMUSG00000091014: 217 Updated longest transcript for ENSMUSG00000031613: 838 Updated longest transcript for ENSMUSG00000021951: 146 Updated longest transcript for ENSMUSG00000093918: 357 Updated longest transcript for ENSMUSG00000094978: 123 Updated longest transcript for ENSMUSG00000063765: 325 Updated longest transcript for ENSMUSG00000037503: 4196 Updated longest transcript for ENSMUSG00000040843: 3454 Updated longest transcript for ENSMUSG00000095172: 138 Updated longest transcript for ENSMUSG00000025877: 2388 Updated longest transcript for ENSMUSG00000057406: 3615 Updated longest transcript for ENSMUSG00000069300: 66 Updated longest transcript for ENSMUSG00000095508: 141 Updated longest transcript for ENSMUSG00000028001: 270 Updated longest transcript for ENSMUSG00000020656: 1456 Updated longest transcript for ENSMUSG00000037926: 4740 Updated longest transcript for ENSMUSG00000049717: 1994 Updated longest transcript for ENSMUSG00000049866: 2861 Updated longest transcript for ENSMUSG00000059821: 1273 Updated longest transcript for ENSMUSG00000014599: 2111 Updated longest transcript for ENSMUSG00000029270: 1351 Updated longest transcript for ENSMUSG00000071866: 582 Updated longest transcript for ENSMUSG00000032178: 1293 Updated longest transcript for ENSMUSG00000021388: 926 Updated longest transcript for ENSMUSG00000092260: 1011 Updated longest transcript for ENSMUSG00000058761: 5048 Updated longest transcript for ENSMUSG00000033228: 3089 Updated longest transcript for ENSMUSG00000067750: 1107 Updated longest transcript for ENSMUSG00000075153: 1598 Updated longest transcript for ENSMUSG00000044951: 4414 Updated longest transcript for ENSMUSG00000032526: 269 Updated longest transcript for ENSMUSG00000071788: 122 Updated longest transcript for ENSMUSG00000068882: 636 Updated longest transcript for ENSMUSG00000029513: 1063 Updated longest transcript for ENSMUSG00000001525: 1061 Updated longest transcript for ENSMUSG00000102483: 445 Updated longest transcript for ENSMUSG00000061665: 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Updated longest transcript for ENSMUSG00000031974: 1849 Updated longest transcript for ENSMUSG00000028608: 2436 Updated longest transcript for ENSMUSG00000061578: 9440 Updated longest transcript for ENSMUSG00000049598: 412 Updated longest transcript for ENSMUSG00000037994: 3087 Updated longest transcript for ENSMUSG00000069072: 6218 Updated longest transcript for ENSMUSG00000006676: 381 Updated longest transcript for ENSMUSG00000053040: 10 Updated longest transcript for ENSMUSG00000022205: 2879 Updated longest transcript for ENSMUSG00000061232: 448 Updated longest transcript for ENSMUSG00000024454: 642 Updated longest transcript for ENSMUSG00000028334: 988 Updated longest transcript for ENSMUSG00000035476: 3915 Updated longest transcript for ENSMUSG00000045509: 690 Updated longest transcript for ENSMUSG00000047473: 976 Updated longest transcript for ENSMUSG00000039693: 666 Updated longest transcript for ENSMUSG00000020633: 982 Updated longest transcript for ENSMUSG00000029530: 3093 Updated longest transcript for ENSMUSG00000027074: 725 Updated longest transcript for ENSMUSG00000027198: 963 Updated longest transcript for ENSMUSG00000028521: 3778 Updated longest transcript for ENSMUSG00000025743: 3510 Updated longest transcript for ENSMUSG00000069184: 2324 Updated longest transcript for ENSMUSG00000022037: 364 Updated longest transcript for ENSMUSG00000047146: 7955 Updated longest transcript for ENSMUSG00000030427: 406 Updated longest transcript for ENSMUSG00000020227: 1067 Updated longest transcript for ENSMUSG00000021490: 1955 Updated longest transcript for ENSMUSG00000025451: 1137 Updated longest transcript for ENSMUSG00000030890: 1482 Updated longest transcript for ENSMUSG00000000690: 640 Updated longest transcript for ENSMUSG00000029063: 1522 Updated longest transcript for ENSMUSG00000072437: 2845 Updated longest transcript for ENSMUSG00000056014: 939 Updated longest transcript for ENSMUSG00000020477: 466 Updated longest transcript for ENSMUSG00000087006: 577 Updated longest transcript for ENSMUSG00000069583: 237 Updated longest transcript for ENSMUSG00000037818: 3754 Updated longest transcript for ENSMUSG00000040340: 492 Updated longest transcript for ENSMUSG00000050105: 240 Updated longest transcript for ENSMUSG00000091407: 7113 Updated longest transcript for ENSMUSG00000020936: 3144 Updated longest transcript for ENSMUSG00000024650: 2088 Updated longest transcript for ENSMUSG00000091722: 3591 Updated longest transcript for ENSMUSG00000022477: 371 Updated longest transcript for ENSMUSG00000037321: 445 Updated longest transcript for ENSMUSG00000013997: 1960 Updated longest transcript for ENSMUSG00000070501: 1922 Updated longest transcript for ENSMUSG00000115170: 2219 Updated longest transcript for ENSMUSG00000043698: 1 Updated longest transcript for ENSMUSG00000091584: 751 Updated longest transcript for ENSMUSG00000032198: 177 Updated longest transcript for ENSMUSG00000037813: 1064 Updated longest transcript for ENSMUSG00000059406: 101 Updated longest transcript for ENSMUSG00000027854: 246 Updated longest transcript for ENSMUSG00000050742: 1690 Updated longest transcript for ENSMUSG00000026917: 1678 Updated longest transcript for ENSMUSG00000018634: 995 Updated longest transcript for ENSMUSG00000075284: 2800 Updated longest transcript for ENSMUSG00000093884: 2497 Updated longest transcript for ENSMUSG00000021932: 519 Updated longest transcript for ENSMUSG00000026028: 3055 Updated longest transcript for ENSMUSG00000035258: 1086 Updated longest transcript for ENSMUSG00000029865: 133 Updated longest transcript for ENSMUSG00000037395: 3213 Updated longest transcript for ENSMUSG00000071178: 86 Updated longest transcript for ENSMUSG00000079012: 330 Updated longest transcript for ENSMUSG00000020519: 147 Updated longest transcript for ENSMUSG00000029372: 656 Updated longest transcript for ENSMUSG00000026687: 1033 Updated longest transcript for ENSMUSG00000032305: 2521 Updated longest transcript for ENSMUSG00000023791: 598 Updated longest transcript for ENSMUSG00000035891: 2429 Updated longest transcript for ENSMUSG00000042351: 4252 Updated longest transcript for ENSMUSG00000025047: 1055 Updated longest transcript for ENSMUSG00000060084: 2542 Updated longest transcript for ENSMUSG00000104614: 255 Updated longest transcript for ENSMUSG00000026439: 316 Updated longest transcript for ENSMUSG00000041650: 767 Updated longest transcript for ENSMUSG00000005131: 240 Updated longest transcript for ENSMUSG00000078908: 3049 Updated longest transcript for ENSMUSG00000039329: 446 Updated longest transcript for ENSMUSG00000021385: 2814 Updated longest transcript for ENSMUSG00000003847: 8365 Updated longest transcript for ENSMUSG00000027550: 2446 Updated longest transcript for ENSMUSG00000069804: 46 Updated longest transcript for ENSMUSG00000035458: 63 Updated longest transcript for ENSMUSG00000035597: 3729 Updated longest transcript for ENSMUSG00000030046: 3185 Updated longest transcript for ENSMUSG00000044881: 1024 Updated longest transcript for ENSMUSG00000033237: 2898 Updated longest transcript for ENSMUSG00000026270: 606 Updated longest transcript for ENSMUSG00000027109: 1639 Updated longest transcript for ENSMUSG00000020485: 491 Updated longest transcript for ENSMUSG00000031410: 232 Updated longest transcript for ENSMUSG00000027782: 6940 Updated longest transcript for ENSMUSG00000021832: 676 Updated longest transcript for ENSMUSG00000040591: 1907 Updated longest transcript for ENSMUSG00000001403: 1598 Updated longest transcript for ENSMUSG00000024544: 1012 Updated longest transcript for ENSMUSG00000051617: 305 Updated longest transcript for ENSMUSG00000023118: 39 Updated longest transcript for ENSMUSG00000037143: 2599 Updated longest transcript for ENSMUSG00000036504: 177 Updated longest transcript for ENSMUSG00000024091: 2362 Updated longest transcript for ENSMUSG00000042345: 5514 Updated longest transcript for ENSMUSG00000094739: 139 Updated longest transcript for ENSMUSG00000021555: 1445 Updated longest transcript for ENSMUSG00000022529: 3122 Updated longest transcript for ENSMUSG00000028776: 463 Updated longest transcript for ENSMUSG00000005339: 206 Updated longest transcript for ENSMUSG00000029811: 355 Updated longest transcript for ENSMUSG00000101874: 1462 Updated longest transcript for ENSMUSG00000070699: 299 Updated longest transcript for ENSMUSG00000023032: 304 Updated longest transcript for ENSMUSG00000008976: 712 Updated longest transcript for ENSMUSG00000055945: 2175 Updated longest transcript for ENSMUSG00000067818: 496 Updated longest transcript for ENSMUSG00000049882: 705 Updated longest transcript for ENSMUSG00000049482: 2747 Updated longest transcript for ENSMUSG00000036721: 2776 Updated longest transcript for ENSMUSG00000059430: 73 Updated longest transcript for ENSMUSG00000032607: 804 Updated longest transcript for ENSMUSG00000024563: 564 Updated longest transcript for ENSMUSG00000062028: 77 Updated longest transcript for ENSMUSG00000050097: 1703 Updated longest transcript for ENSMUSG00000020804: 978 Updated longest transcript for ENSMUSG00000075380: 1990 Updated longest transcript for ENSMUSG00000022754: 1522 Updated longest transcript for ENSMUSG00000068823: 2247 Updated longest transcript for ENSMUSG00000005732: 285 Updated longest transcript for ENSMUSG00000022002: 118 Updated longest transcript for ENSMUSG00000029363: 1707 Updated longest transcript for ENSMUSG00000029863: 2031 Updated longest transcript for ENSMUSG00000029462: 2062 Updated longest transcript for ENSMUSG00000054054: 1711 Updated longest transcript for ENSMUSG00000028786: 238 Updated longest transcript for ENSMUSG00000110641: 29 Updated longest transcript for ENSMUSG00000052468: 899 Updated longest transcript for ENSMUSG00000030037: 266 Updated longest transcript for ENSMUSG00000016552: 2638 Updated longest transcript for ENSMUSG00000046808: 1529 Updated longest transcript for ENSMUSG00000047751: 122 Updated longest transcript for ENSMUSG00000068959: 2387 Updated longest transcript for ENSMUSG00000066842: 163 Updated longest transcript for ENSMUSG00000033594: 1013 Updated longest transcript for ENSMUSG00000022892: 5646 Updated longest transcript for ENSMUSG00000063681: 1516 Updated longest transcript for ENSMUSG00000035575: 2002 Updated longest transcript for ENSMUSG00000024878: 677 Updated longest transcript for ENSMUSG00000000204: 915 Updated longest transcript for ENSMUSG00000045514: 85 Updated longest transcript for ENSMUSG00000095360: 319 Updated longest transcript for ENSMUSG00000020516: 3854 Updated longest transcript for ENSMUSG00000020728: 419 Updated longest transcript for ENSMUSG00000029131: 1365 Updated longest transcript for ENSMUSG00000043929: 4259 Updated longest transcript for ENSMUSG00000017734: 2984 Updated longest transcript for ENSMUSG00000030827: 134 Updated longest transcript for ENSMUSG00000032481: 2396 Updated longest transcript for ENSMUSG00000076490: 216 Updated longest transcript for ENSMUSG00000049649: 1027 Updated longest transcript for ENSMUSG00000005621: 3301 Updated longest transcript for ENSMUSG00000024998: 2528 Updated longest transcript for ENSMUSG00000069456: 2209 Updated longest transcript for ENSMUSG00000020932: 1341 Updated longest transcript for ENSMUSG00000049235: 310 Updated longest transcript for ENSMUSG00000089872: 3842 Updated longest transcript for ENSMUSG00000049287: 2896 Updated longest transcript for ENSMUSG00000036270: 603 Updated longest transcript for ENSMUSG00000025265: 613 Updated longest transcript for ENSMUSG00000039628: 842 Updated longest transcript for ENSMUSG00000109238: 129 Updated longest transcript for ENSMUSG00000031990: 982 Updated longest transcript for ENSMUSG00000041423: 578 Updated longest transcript for ENSMUSG00000026031: 4962 Updated longest transcript for ENSMUSG00000029027: 921 Updated longest transcript for ENSMUSG00000025245: 5669 Updated longest transcript for ENSMUSG00000036904: 3159 Updated longest transcript for ENSMUSG00000052040: 5157 Updated longest transcript for ENSMUSG00000030359: 137 Updated longest transcript for ENSMUSG00000096576: 1480 Updated longest transcript for ENSMUSG00000072423: 3321 Updated longest transcript for ENSMUSG00000078253: 358 Updated longest transcript for ENSMUSG00000033253: 1974 Updated longest transcript for ENSMUSG00000070601: 7875 Updated longest transcript for ENSMUSG00000058579: 76 Updated longest transcript for ENSMUSG00000048874: 2603 Updated longest transcript for ENSMUSG00000060739: 1737 Updated longest transcript for ENSMUSG00000000673: 487 Updated longest transcript for ENSMUSG00000032318: 936 Updated longest transcript for ENSMUSG00000095257: 121 Updated longest transcript for ENSMUSG00000096457: 122 Updated longest transcript for ENSMUSG00000090643: 959 Updated longest transcript for ENSMUSG00000030450: 2870 Updated longest transcript for ENSMUSG00000024868: 1322 Updated longest transcript for ENSMUSG00000063049: 1488 Updated longest transcript for ENSMUSG00000005824: 1019 Updated longest transcript for ENSMUSG00000042719: 2459 Updated longest transcript for ENSMUSG00000059518: 232 Updated longest transcript for ENSMUSG00000075117: 1990 Updated longest transcript for ENSMUSG00000001783: 375 Updated longest transcript for ENSMUSG00000022112: 742 Updated longest transcript for ENSMUSG00000039608: 2267 Updated longest transcript for ENSMUSG00000078934: 1261 Updated longest transcript for ENSMUSG00000024922: 1822 Updated longest transcript for ENSMUSG00000015120: 1891 Updated longest transcript for ENSMUSG00000031939: 1342 Updated longest transcript for ENSMUSG00000060090: 3118 Updated longest transcript for ENSMUSG00000037266: 624 Updated longest transcript for ENSMUSG00000027209: 145 Updated longest transcript for ENSMUSG00000015599: 6590 Updated longest transcript for ENSMUSG00000071528: 105 Updated longest transcript for ENSMUSG00000055567: 3552 Updated longest transcript for ENSMUSG00000025492: 139 Updated longest transcript for ENSMUSG00000068184: 2212 Updated longest transcript for ENSMUSG00000041710: 827 Updated longest transcript for ENSMUSG00000063844: 3971 Updated longest transcript for ENSMUSG00000066797: 15 Updated longest transcript for ENSMUSG00000001542: 1568 Updated longest transcript for ENSMUSG00000032087: 799 Updated longest transcript for ENSMUSG00000046598: 403 Updated longest transcript for ENSMUSG00000035390: 507 Updated longest transcript for ENSMUSG00000089715: 4084 Updated longest transcript for ENSMUSG00000014543: 214 Updated longest transcript for ENSMUSG00000103711: 110 Updated longest transcript for ENSMUSG00000020196: 606 Updated longest transcript for ENSMUSG00000022146: 334 Updated longest transcript for ENSMUSG00000047940: 164 Updated longest transcript for ENSMUSG00000039660: 622 Updated longest transcript for ENSMUSG00000021532: 118 Updated longest transcript for ENSMUSG00000028690: 1178 Updated longest transcript for ENSMUSG00000104824: 1985 Updated longest transcript for ENSMUSG00000052031: 767 Updated longest transcript for ENSMUSG00000062593: 859 Updated longest transcript for ENSMUSG00000024018: 2789 Updated longest transcript for ENSMUSG00000057402: 284 Updated longest transcript for ENSMUSG00000039215: 113 Updated longest transcript for ENSMUSG00000020641: 3613 Updated longest transcript for ENSMUSG00000029649: 925 Updated longest transcript for ENSMUSG00000096316: 92 Updated longest transcript for ENSMUSG00000110576: 115 Updated longest transcript for ENSMUSG00000071632: 945 Updated longest transcript for ENSMUSG00000027499: 3276 Updated longest transcript for ENSMUSG00000021752: 688 Updated longest transcript for ENSMUSG00000015002: 2558 Updated longest transcript for ENSMUSG00000026235: 3310 Updated longest transcript for ENSMUSG00000002055: 258 Updated longest transcript for ENSMUSG00000066438: 1729 Updated longest transcript for ENSMUSG00000048537: 1254 Updated longest transcript for ENSMUSG00000043430: 925 Updated longest transcript for ENSMUSG00000026260: 1302 Updated longest transcript for ENSMUSG00000032085: 752 Updated longest transcript for ENSMUSG00000036550: 2397 Updated longest transcript for ENSMUSG00000032508: 987 Updated longest transcript for ENSMUSG00000031293: 4992 Updated longest transcript for ENSMUSG00000004798: 2132 Updated longest transcript for ENSMUSG00000019230: 3468 Updated longest transcript for ENSMUSG00000055692: 485 Updated longest transcript for ENSMUSG00000024534: 2098 Updated longest transcript for ENSMUSG00000037989: 1265 Updated longest transcript for ENSMUSG00000019087: 893 Updated longest transcript for ENSMUSG00000089678: 596 Updated longest transcript for ENSMUSG00000001225: 856 Updated longest transcript for ENSMUSG00000066721: 1657 Updated longest transcript for ENSMUSG00000064262: 1019 Updated longest transcript for ENSMUSG00000036304: 4113 Updated longest transcript for ENSMUSG00000026280: 2851 Updated longest transcript for ENSMUSG00000055633: 388 Updated longest transcript for ENSMUSG00000026131: 1756 Updated longest transcript for ENSMUSG00000029093: 2148 Updated longest transcript for ENSMUSG00000031950: 1207 Updated longest transcript for ENSMUSG00000033720: 2620 Updated longest transcript for ENSMUSG00000035242: 370 Updated longest transcript for ENSMUSG00000027703: 6 Updated longest transcript for ENSMUSG00000025380: 59 Updated longest transcript for ENSMUSG00000003269: 492 Updated longest transcript for ENSMUSG00000031757: 164 Updated longest transcript for ENSMUSG00000102312: 2401 Updated longest transcript for ENSMUSG00000030007: 1239 Updated longest transcript for ENSMUSG00000029490: 1904 Updated longest transcript for ENSMUSG00000100708: 196 Updated longest transcript for ENSMUSG00000024208: 50 Updated longest transcript for ENSMUSG00000026563: 901 Updated longest transcript for ENSMUSG00000027618: 625 Updated longest transcript for ENSMUSG00000037712: 1055 Updated longest transcript for ENSMUSG00000020052: 1213 Updated longest transcript for ENSMUSG00000021098: 1569 Updated longest transcript for ENSMUSG00000031901: 1476 Updated longest transcript for ENSMUSG00000021939: 3594 Updated longest transcript for ENSMUSG00000009376: 2157 Updated longest transcript for ENSMUSG00000032870: 1222 Updated longest transcript for ENSMUSG00000031285: 1769 Updated longest transcript for ENSMUSG00000029547: 236 Updated longest transcript for ENSMUSG00000004661: 2160 Updated longest transcript for ENSMUSG00000038393: 1354 Updated longest transcript for ENSMUSG00000033124: 3298 Updated longest transcript for ENSMUSG00000024771: 1931 Updated longest transcript for ENSMUSG00000060284: 1531 Updated longest transcript for ENSMUSG00000026649: 182 Updated longest transcript for ENSMUSG00000091844: 80 Updated longest transcript for ENSMUSG00000066964: 987 Updated longest transcript for ENSMUSG00000032558: 1686 Updated longest transcript for ENSMUSG00000038172: 6834 Updated longest transcript for ENSMUSG00000029387: 1693 Updated longest transcript for ENSMUSG00000074582: 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83 Updated longest transcript for ENSMUSG00000075297: 100 Updated longest transcript for ENSMUSG00000074247: 1444 Updated longest transcript for ENSMUSG00000025726: 804 Updated longest transcript for ENSMUSG00000038046: 195 Updated longest transcript for ENSMUSG00000032470: 426 Updated longest transcript for ENSMUSG00000051095: 2079 Updated longest transcript for ENSMUSG00000037390: 522 Updated longest transcript for ENSMUSG00000041328: 1019 Updated longest transcript for ENSMUSG00000021327: 4649 Updated longest transcript for ENSMUSG00000027272: 2381 Updated longest transcript for ENSMUSG00000050751: 1442 Updated longest transcript for ENSMUSG00000043312: 62 Updated longest transcript for ENSMUSG00000024176: 1441 Updated longest transcript for ENSMUSG00000071281: 2677 Updated longest transcript for ENSMUSG00000034087: 525 Updated longest transcript for ENSMUSG00000029530: 3966 Updated longest transcript for ENSMUSG00000073856: 2562 Updated longest transcript for ENSMUSG00000050855: 1933 Updated longest transcript for ENSMUSG00000021748: 340 Updated longest transcript for ENSMUSG00000019809: 57 Updated longest transcript for ENSMUSG00000008540: 364 Updated longest transcript for ENSMUSG00000090572: 548 Updated longest transcript for ENSMUSG00000017667: 5818 Updated longest transcript for ENSMUSG00000038702: 474 Updated longest transcript for ENSMUSG00000038044: 50 Updated longest transcript for ENSMUSG00000004846: 387 Updated longest transcript for ENSMUSG00000096220: 2450 Updated longest transcript for ENSMUSG00000004359: 872 Updated longest transcript for ENSMUSG00000046160: 1274 Updated longest transcript for ENSMUSG00000098810: 1187 Updated longest transcript for ENSMUSG00000005718: 892 Updated longest transcript for ENSMUSG00000030091: 1331 Updated longest transcript for ENSMUSG00000026869: 2842 Updated longest transcript for ENSMUSG00000035142: 144 Updated longest transcript for ENSMUSG00000047108: 270 Updated longest transcript for ENSMUSG00000048280: 10804 Updated longest transcript for ENSMUSG00000026279: 3601 Updated longest transcript for ENSMUSG00000071646: 648 Updated longest transcript for ENSMUSG00000030553: 329 Updated longest transcript for ENSMUSG00000101026: 1213 Updated longest transcript for ENSMUSG00000020185: 2546 Updated longest transcript for ENSMUSG00000034164: 711 Updated longest transcript for ENSMUSG00000028602: 2194 Updated longest transcript for ENSMUSG00000114432: 978 Updated longest transcript for ENSMUSG00000030270: 1810 Updated longest transcript for ENSMUSG00000090693: 339 Updated longest transcript for ENSMUSG00000037924: 91 Updated longest transcript for ENSMUSG00000042682: 1183 Updated longest transcript for ENSMUSG00000038528: 1988 Updated longest transcript for ENSMUSG00000002679: 349 Updated longest transcript for ENSMUSG00000024121: 538 Updated longest transcript for ENSMUSG00000062369: 55 Updated longest transcript for ENSMUSG00000022887: 2965 Updated longest transcript for ENSMUSG00000078759: 120 Updated longest transcript for ENSMUSG00000021708: 1032 Updated longest transcript for ENSMUSG00000025469: 1366 Updated longest transcript for ENSMUSG00000031872: 1855 Updated longest transcript for ENSMUSG00000051212: 1483 Updated longest transcript for ENSMUSG00000058587: 2409 Updated longest transcript for ENSMUSG00000060679: 620 Updated longest transcript for ENSMUSG00000025497: 64 Updated longest transcript for ENSMUSG00000032303: 2690 Updated longest transcript for ENSMUSG00000045493: 1691 Updated longest transcript for ENSMUSG00000109350: 175 Updated longest transcript for ENSMUSG00000048458: 3628 Updated longest transcript for ENSMUSG00000031906: 369 Updated longest transcript for ENSMUSG00000046168: 1189 Updated longest transcript for ENSMUSG00000033316: 1060 Updated longest transcript for ENSMUSG00000056755: 1163 Updated longest transcript for ENSMUSG00000044176: 393 Updated longest transcript for ENSMUSG00000075137: 613 Updated longest transcript for ENSMUSG00000054679: 2047 Updated longest transcript for ENSMUSG00000034430: 2558 Updated longest transcript for ENSMUSG00000023939: 81 Updated longest transcript for ENSMUSG00000006800: 874 Updated longest transcript for ENSMUSG00000010362: 603 Updated longest transcript for ENSMUSG00000030945: 4681 Updated longest transcript for ENSMUSG00000009108: 629 Updated longest transcript for ENSMUSG00000030742: 222 Updated longest transcript for ENSMUSG00000018372: 346 Updated longest transcript for ENSMUSG00000061246: 325 Updated longest transcript for ENSMUSG00000042708: 1048 Updated longest transcript for ENSMUSG00000115232: 2844 Updated longest transcript for ENSMUSG00000002804: 519 Updated longest transcript for ENSMUSG00000053182: 1131 Updated longest transcript for ENSMUSG00000025903: 1723 Updated longest transcript for ENSMUSG00000091185: 838 Updated longest transcript for ENSMUSG00000057667: 2528 Updated longest transcript for ENSMUSG00000054280: 3712 Updated longest transcript for ENSMUSG00000027204: 992 Updated longest transcript for ENSMUSG00000032096: 2363 Updated longest transcript for ENSMUSG00000111372: 509 Updated longest transcript for ENSMUSG00000028407: 798 Updated longest transcript for ENSMUSG00000073998: 304 Updated longest transcript for ENSMUSG00000021510: 6424 Updated longest transcript for ENSMUSG00000071649: 402 Updated longest transcript for ENSMUSG00000013701: 398 Updated longest transcript for ENSMUSG00000095595: 3125 Updated longest transcript for ENSMUSG00000070942: 2117 Updated longest transcript for ENSMUSG00000040752: 60 Updated longest transcript for ENSMUSG00000033589: 106 Updated longest transcript for ENSMUSG00000034810: 2073 Updated longest transcript for ENSMUSG00000028435: 812 Updated longest transcript for ENSMUSG00000032898: 2004 Updated longest transcript for ENSMUSG00000022843: 1066 Updated longest transcript for ENSMUSG00000069733: 145 Updated longest transcript for ENSMUSG00000118653: 159 Updated longest transcript for ENSMUSG00000033578: 1129 Updated longest transcript for ENSMUSG00000092342: 841 Updated longest transcript for ENSMUSG00000047502: 221 Updated longest transcript for ENSMUSG00000036381: 396 Updated longest transcript for ENSMUSG00000071856: 4827 Updated longest transcript for ENSMUSG00000074165: 934 Updated longest transcript for ENSMUSG00000048013: 315 Updated longest transcript for ENSMUSG00000048185: 36 Updated longest transcript for ENSMUSG00000004568: 2066 Updated longest transcript for ENSMUSG00000026313: 590 Updated longest transcript for ENSMUSG00000022490: 665 Updated longest transcript for ENSMUSG00000048376: 1930 Updated longest transcript for ENSMUSG00000056220: 443 Updated longest transcript for ENSMUSG00000079164: 290 Updated longest transcript for ENSMUSG00000026768: 3065 Updated longest transcript for ENSMUSG00000003992: 2734 Updated longest transcript for ENSMUSG00000063838: 75 Updated longest transcript for ENSMUSG00000034000: 2918 Updated longest transcript for ENSMUSG00000022641: 5715 Updated longest transcript for ENSMUSG00000014980: 528 Updated longest transcript for ENSMUSG00000078495: 775 Updated longest transcript for ENSMUSG00000002846: 586 Updated longest transcript for ENSMUSG00000024835: 1221 Updated longest transcript for ENSMUSG00000059866: 600 Updated longest transcript for ENSMUSG00000049470: 6315 Updated longest transcript for ENSMUSG00000055826: 121 Updated longest transcript for ENSMUSG00000027811: 1536 Updated longest transcript for ENSMUSG00000075161: 1262 Updated longest transcript for ENSMUSG00000029014: 424 Updated longest transcript for ENSMUSG00000006398: 62 Updated longest transcript for ENSMUSG00000022556: 1505 Updated longest transcript for ENSMUSG00000028949: 129 Updated longest transcript for ENSMUSG00000062132: 107 Updated longest transcript for ENSMUSG00000055489: 60 Updated longest transcript for ENSMUSG00000018166: 1845 Updated longest transcript for ENSMUSG00000035694: 951 Updated longest transcript for ENSMUSG00000019834: 186 Updated longest transcript for ENSMUSG00000026336: 194 Updated longest transcript for ENSMUSG00000001943: 393 Updated longest transcript for ENSMUSG00000078566: 95 Updated longest transcript for ENSMUSG00000037957: 780 Updated longest transcript for ENSMUSG00000026866: 1244 Updated longest transcript for ENSMUSG00000021298: 1183 Updated longest transcript for ENSMUSG00000022296: 3684 Updated longest transcript for ENSMUSG00000022354: 82 Updated longest transcript for ENSMUSG00000027406: 209 Updated longest transcript for ENSMUSG00000024486: 1456 Updated longest transcript for ENSMUSG00000074269: 86 Updated longest transcript for ENSMUSG00000019489: 78 Updated longest transcript for ENSMUSG00000030321: 1021 Updated longest transcript for ENSMUSG00000025995: 104 Updated longest transcript for ENSMUSG00000031527: 3875 Updated longest transcript for ENSMUSG00000044021: 176 Updated longest transcript for ENSMUSG00000074748: 2999 Updated longest transcript for ENSMUSG00000038403: 520 Updated longest transcript for ENSMUSG00000060093: 154 Updated longest transcript for ENSMUSG00000022100: 11755 Updated longest transcript for ENSMUSG00000020878: 61 Updated longest transcript for ENSMUSG00000020492: 2636 Updated longest transcript for ENSMUSG00000038205: 4007 Updated longest transcript for ENSMUSG00000040724: 367 Updated longest transcript for ENSMUSG00000048997: 137 Updated longest transcript for ENSMUSG00000006715: 224 Updated longest transcript for ENSMUSG00000033308: 198 Updated longest transcript for ENSMUSG00000032423: 7245 Updated longest transcript for ENSMUSG00000028229: 809 Updated longest transcript for ENSMUSG00000032194: 1509 Updated longest transcript for ENSMUSG00000032473: 3262 Updated longest transcript for ENSMUSG00000079120: 163 Updated longest transcript for ENSMUSG00000045232: 164 Updated longest transcript for ENSMUSG00000097239: 225 Updated longest transcript for ENSMUSG00000003032: 982 Updated longest transcript for ENSMUSG00000034793: 275 Updated longest transcript for ENSMUSG00000031351: 1871 Updated longest transcript for ENSMUSG00000040197: 1119 Updated longest transcript for ENSMUSG00000006675: 125 Updated longest transcript for ENSMUSG00000053930: 5747 Updated longest transcript for ENSMUSG00000032567: 529 Updated longest transcript for ENSMUSG00000005583: 4449 Updated longest transcript for ENSMUSG00000090862: 929 Updated longest transcript for ENSMUSG00000057116: 1348 Updated longest transcript for ENSMUSG00000045912: 5198 Updated longest transcript for ENSMUSG00000025420: 306 Updated longest transcript for ENSMUSG00000020032: 3341 Updated longest transcript for ENSMUSG00000032867: 3120 Updated longest transcript for ENSMUSG00000031546: 463 Updated longest transcript for ENSMUSG00000059040: 351 Updated longest transcript for ENSMUSG00000013878: 2791 Updated longest transcript for ENSMUSG00000043671: 5616 Updated longest transcript for ENSMUSG00000073601: 428 Updated longest transcript for ENSMUSG00000026417: 1353 Updated longest transcript for ENSMUSG00000019789: 1377 Updated longest transcript for ENSMUSG00000097064: 1014 Updated longest transcript for ENSMUSG00000095809: 1690 Updated longest transcript for ENSMUSG00000057710: 219 Updated longest transcript for ENSMUSG00000070990: 1103 Updated longest transcript for ENSMUSG00000035842: 1329 Updated longest transcript for ENSMUSG00000039497: 823 Updated longest transcript for ENSMUSG00000052508: 2685 Updated longest transcript for ENSMUSG00000029032: 470 Updated longest transcript for ENSMUSG00000030611: 645 Updated longest transcript for ENSMUSG00000031428: 297 Updated longest transcript for ENSMUSG00000034424: 819 Updated longest transcript for ENSMUSG00000094682: 394 Updated longest transcript for ENSMUSG00000058402: 734 Updated longest transcript for ENSMUSG00000062649: 2521 Updated longest transcript for ENSMUSG00000017548: 1933 Updated longest transcript for ENSMUSG00000029778: 4280 Updated longest transcript for ENSMUSG00000027022: 1883 Updated longest transcript for ENSMUSG00000061601: 1961 Updated longest transcript for ENSMUSG00000054385: 2175 Updated longest transcript for ENSMUSG00000040966: 384 Updated longest transcript for ENSMUSG00000029651: 2546 Updated longest transcript for ENSMUSG00000054206: 101 Updated longest transcript for ENSMUSG00000022204: 129 Updated longest transcript for ENSMUSG00000001138: 3007 Updated longest transcript for ENSMUSG00000025955: 578 Updated longest transcript for ENSMUSG00000035150: 2152 Updated longest transcript for ENSMUSG00000031283: 2648 Updated longest transcript for ENSMUSG00000037661: 401 Updated longest transcript for ENSMUSG00000049625: 2481 Updated longest transcript for ENSMUSG00000046523: 2836 Updated longest transcript for ENSMUSG00000067297: 1765 Updated longest transcript for ENSMUSG00000002052: 837 Updated longest transcript for ENSMUSG00000028066: 374 Updated longest transcript for ENSMUSG00000023791: 633 Updated longest transcript for ENSMUSG00000036565: 2938 Updated longest transcript for ENSMUSG00000037572: 801 Updated longest transcript for ENSMUSG00000031778: 566 Updated longest transcript for ENSMUSG00000002210: 400 Updated longest transcript for ENSMUSG00000040125: 10089 Updated longest transcript for ENSMUSG00000058400: 877 Updated longest transcript for ENSMUSG00000022270: 1641 Updated longest transcript for ENSMUSG00000020876: 1169 Updated longest transcript for ENSMUSG00000025500: 81 Updated longest transcript for ENSMUSG00000047146: 7962 Updated longest transcript for ENSMUSG00000003435: 230 Updated longest transcript for ENSMUSG00000108931: 676 Updated longest transcript for ENSMUSG00000031059: 93 Updated longest transcript for ENSMUSG00000020952: 171 Updated longest transcript for ENSMUSG00000090066: 4380 Updated longest transcript for ENSMUSG00000073876: 354 Updated longest transcript for ENSMUSG00000094493: 115 Updated longest transcript for ENSMUSG00000035699: 1238 Updated longest transcript for ENSMUSG00000004591: 668 Updated longest transcript for ENSMUSG00000109446: 187 Updated longest transcript for ENSMUSG00000036087: 2766 Updated longest transcript for ENSMUSG00000038884: 536 Updated longest transcript for ENSMUSG00000097733: 204 Updated longest transcript for ENSMUSG00000003053: 2415 Updated longest transcript for ENSMUSG00000032238: 9285 Updated longest transcript for ENSMUSG00000024082: 1526 Updated longest transcript for ENSMUSG00000069208: 460 Updated longest transcript for ENSMUSG00000022466: 197 Updated longest transcript for ENSMUSG00000047898: 1597 Updated longest transcript for ENSMUSG00000025979: 2093 Updated longest transcript for ENSMUSG00000021326: 2099 Updated longest transcript for ENSMUSG00000037536: 3352 Updated longest transcript for ENSMUSG00000001663: 226 Updated longest transcript for ENSMUSG00000068855: 94 Updated longest transcript for ENSMUSG00000062148: 126 Updated longest transcript for ENSMUSG00000041550: 784 Updated longest transcript for ENSMUSG00000053702: 4640 Updated longest transcript for ENSMUSG00000040429: 146 Updated longest transcript for ENSMUSG00000091694: 919 Updated longest transcript for ENSMUSG00000025497: 158 Updated longest transcript for ENSMUSG00000029403: 335 Updated longest transcript for ENSMUSG00000075031: 66 Updated longest transcript for ENSMUSG00000020225: 342 Updated longest transcript for ENSMUSG00000041377: 345 Updated longest transcript for ENSMUSG00000027577: 2443 Updated longest transcript for ENSMUSG00000118396: 60 Updated longest transcript for ENSMUSG00000026365: 1618 Updated longest transcript for ENSMUSG00000074344: 189 Updated longest transcript for ENSMUSG00000016409: 2781 Updated longest transcript for ENSMUSG00000048981: 261 Updated longest transcript for ENSMUSG00000023274: 1553 Updated longest transcript for ENSMUSG00000050410: 444 Updated longest transcript for ENSMUSG00000078889: 3111 Updated longest transcript for ENSMUSG00000036197: 3114 Updated longest transcript for ENSMUSG00000047702: 1315 Updated longest transcript for ENSMUSG00000032777: 2387 Updated longest transcript for ENSMUSG00000090824: 29 Updated longest transcript for ENSMUSG00000055963: 355 Updated longest transcript for ENSMUSG00000109179: 1474 Updated longest transcript for ENSMUSG00000058523: 271 Updated longest transcript for ENSMUSG00000027660: 3960 Updated longest transcript for ENSMUSG00000017943: 1301 Updated longest transcript for ENSMUSG00000079539: 153 Updated longest transcript for ENSMUSG00000055976: 730 Updated longest transcript for ENSMUSG00000075377: 2989 Updated longest transcript for ENSMUSG00000075122: 1896 Updated longest transcript for ENSMUSG00000095852: 354 Updated longest transcript for ENSMUSG00000049605: 2885 Updated longest transcript for ENSMUSG00000027605: 1271 Updated longest transcript for ENSMUSG00000023572: 391 Updated longest transcript for ENSMUSG00000024856: 182 Updated longest transcript for ENSMUSG00000045348: 1783 Updated longest transcript for ENSMUSG00000067562: 517 Updated longest transcript for ENSMUSG00000026331: 594 Updated longest transcript for ENSMUSG00000022878: 434 Updated longest transcript for ENSMUSG00000040522: 22 Updated longest transcript for ENSMUSG00000026255: 1044 Updated longest transcript for ENSMUSG00000098332: 103 Updated longest transcript for ENSMUSG00000098773: 565 Updated longest transcript for ENSMUSG00000034544: 2083 Updated longest transcript for ENSMUSG00000018809: 786 Updated longest transcript for ENSMUSG00000090291: 1066 Updated longest transcript for ENSMUSG00000030341: 299 Updated longest transcript for ENSMUSG00000022752: 3969 Updated longest transcript for ENSMUSG00000025038: 216 Updated longest transcript for ENSMUSG00000037337: 112 Updated longest transcript for ENSMUSG00000072720: 2590 Updated longest transcript for ENSMUSG00000023984: 4282 Updated longest transcript for ENSMUSG00000078302: 3315 Updated longest transcript for ENSMUSG00000029193: 1980 Updated longest transcript for ENSMUSG00000026011: 1115 Updated longest transcript for ENSMUSG00000060227: 2359 Updated longest transcript for ENSMUSG00000037904: 1166 Updated longest transcript for ENSMUSG00000096276: 1468 Updated longest transcript for ENSMUSG00000024560: 288 Updated longest transcript for ENSMUSG00000044624: 505 Updated longest transcript for ENSMUSG00000014704: 469 Updated longest transcript for ENSMUSG00000041977: 3637 Updated longest transcript for ENSMUSG00000091227: 959 Updated longest transcript for ENSMUSG00000034653: 2532 Updated longest transcript for ENSMUSG00000059077: 237 Updated longest transcript for ENSMUSG00000015484: 2931 Updated longest transcript for ENSMUSG00000019368: 1681 Updated longest transcript for ENSMUSG00000022793: 1019 Updated longest transcript for ENSMUSG00000095546: 125 Updated longest transcript for ENSMUSG00000030050: 625 Updated longest transcript for ENSMUSG00000025486: 762 Updated longest transcript for ENSMUSG00000005533: 7267 Updated longest transcript for ENSMUSG00000056174: 2032 Updated longest transcript for ENSMUSG00000045996: 231 Updated longest transcript for ENSMUSG00000043119: 1700 Updated longest transcript for ENSMUSG00000070000: 429 Updated longest transcript for ENSMUSG00000019761: 376 Updated longest transcript for ENSMUSG00000048720: 1984 Updated longest transcript for ENSMUSG00000096294: 942 Updated longest transcript for ENSMUSG00000048834: 2802 Updated longest transcript for ENSMUSG00000041879: 3960 Updated longest transcript for ENSMUSG00000021381: 515 Updated longest transcript for ENSMUSG00000025616: 1470 Updated longest transcript for ENSMUSG00000118318: 7863 Updated longest transcript for ENSMUSG00000030748: 2767 Updated longest transcript for ENSMUSG00000002249: 1045 Updated longest transcript for ENSMUSG00000046269: 1993 Updated longest transcript for ENSMUSG00000070733: 1717 Updated longest transcript for ENSMUSG00000053765: 657 Updated longest transcript for ENSMUSG00000015714: 810 Updated longest transcript for ENSMUSG00000033323: 675 Updated longest transcript for ENSMUSG00000020904: 106 Updated longest transcript for ENSMUSG00000009569: 4834 Updated longest transcript for ENSMUSG00000078688: 273 Updated longest transcript for ENSMUSG00000052751: 1137 Updated longest transcript for ENSMUSG00000041841: 504 Updated longest transcript for ENSMUSG00000021413: 4468 Updated longest transcript for ENSMUSG00000037347: 595 Updated longest transcript for ENSMUSG00000071350: 749 Updated longest transcript for ENSMUSG00000021991: 322 Updated longest transcript for ENSMUSG00000039209: 446 Updated longest transcript for ENSMUSG00000025538: 1327 Updated longest transcript for ENSMUSG00000029428: 1918 Updated longest transcript for ENSMUSG00000035238: 872 Updated longest transcript for ENSMUSG00000030279: 3749 Updated longest transcript for ENSMUSG00000107928: 4332 Updated longest transcript for ENSMUSG00000040102: 4632 Updated longest transcript for ENSMUSG00000053909: 50 Updated longest transcript for ENSMUSG00000071311: 307 Updated longest transcript for ENSMUSG00000099687: 51 Updated longest transcript for ENSMUSG00000024856: 639 Updated longest transcript for ENSMUSG00000097297: 3689 Updated longest transcript for ENSMUSG00000037849: 206 Updated longest transcript for ENSMUSG00000049299: 226 Updated longest transcript for ENSMUSG00000018160: 3086 Updated longest transcript for ENSMUSG00000062939: 253 Updated longest transcript for ENSMUSG00000017316: 143 Updated longest transcript for ENSMUSG00000073978: 3008 Updated longest transcript for ENSMUSG00000027865: 1162 Updated longest transcript for ENSMUSG00000032191: 516 Updated longest transcript for ENSMUSG00000050473: 924 Updated longest transcript for ENSMUSG00000103468: 127 Updated longest transcript for ENSMUSG00000028004: 1935 Updated longest transcript for ENSMUSG00000014158: 501 Updated longest transcript for ENSMUSG00000072872: 3608 Updated longest transcript for ENSMUSG00000038233: 1777 Updated longest transcript for ENSMUSG00000034121: 778 Updated longest transcript for ENSMUSG00000024077: 5577 Updated longest transcript for ENSMUSG00000032344: 2399 Updated longest transcript for ENSMUSG00000100967: 3540 Updated longest transcript for ENSMUSG00000039648: 380 Updated longest transcript for ENSMUSG00000030532: 1181 Updated longest transcript for ENSMUSG00000044716: 1191 Updated longest transcript for ENSMUSG00000031010: 3844 Updated longest transcript for ENSMUSG00000033632: 3500 Updated longest transcript for ENSMUSG00000095901: 706 Updated longest transcript for ENSMUSG00000071489: 1991 Updated longest transcript for ENSMUSG00000067971: 3396 Updated longest transcript for ENSMUSG00000069971: 328 Updated longest transcript for ENSMUSG00000014763: 1683 Updated longest transcript for ENSMUSG00000061298: 1962 Updated longest transcript for ENSMUSG00000026679: 214 Updated longest transcript for ENSMUSG00000026790: 1085 Updated longest transcript for ENSMUSG00000018501: 1153 Updated longest transcript for ENSMUSG00000060152: 1059 Updated longest transcript for ENSMUSG00000074527: 4701 Updated longest transcript for ENSMUSG00000027634: 3179 Updated longest transcript for ENSMUSG00000102222: 1744 Updated longest transcript for ENSMUSG00000026426: 998 Updated longest transcript for ENSMUSG00000061937: 309 Updated longest transcript for ENSMUSG00000037747: 1565 Updated longest transcript for ENSMUSG00000055681: 84 Updated longest transcript for ENSMUSG00000056121: 2660 Updated longest transcript for ENSMUSG00000074156: 181 Updated longest transcript for ENSMUSG00000050390: 1877 Updated longest transcript for ENSMUSG00000025151: 212 Updated longest transcript for ENSMUSG00000026866: 1737 Updated longest transcript for ENSMUSG00000036138: 423 Updated longest transcript for ENSMUSG00000034412: 349 Updated longest transcript for ENSMUSG00000056069: 2025 Updated longest transcript for ENSMUSG00000075189: 100 Updated longest transcript for ENSMUSG00000039089: 1652 Updated longest transcript for ENSMUSG00000024393: 380 Updated longest transcript for ENSMUSG00000040396: 3737 Updated longest transcript for ENSMUSG00000040640: 2972 Updated longest transcript for ENSMUSG00000081234: 3396 Updated longest transcript for ENSMUSG00000069633: 2095 Updated longest transcript for ENSMUSG00000059910: 4364 Updated longest transcript for ENSMUSG00000109354: 111 Updated longest transcript for ENSMUSG00000039269: 136 Updated longest transcript for ENSMUSG00000073396: 655 Updated longest transcript for ENSMUSG00000031200: 692 Updated longest transcript for ENSMUSG00000023170: 158 Updated longest transcript for ENSMUSG00000026237: 22 Updated longest transcript for ENSMUSG00000039630: 5011 Updated longest transcript for ENSMUSG00000001444: 701 Updated longest transcript for ENSMUSG00000059456: 797 Updated longest transcript for ENSMUSG00000034120: 561 Updated longest transcript for ENSMUSG00000053411: 2286 Updated longest transcript for ENSMUSG00000048148: 3890 Updated longest transcript for ENSMUSG00000044339: 22 Updated longest transcript for ENSMUSG00000036120: 127 Updated longest transcript for ENSMUSG00000102053: 361 Updated longest transcript for ENSMUSG00000066772: 433 Updated longest transcript for ENSMUSG00000040651: 2858 Updated longest transcript for ENSMUSG00000028710: 1841 Updated longest transcript for ENSMUSG00000040904: 1254 Updated longest transcript for ENSMUSG00000051493: 1976 Updated longest transcript for ENSMUSG00000055594: 92 Updated longest transcript for ENSMUSG00000030922: 496 Updated longest transcript for ENSMUSG00000089762: 1254 Updated longest transcript for ENSMUSG00000042978: 2367 Updated longest transcript for ENSMUSG00000115972: 1680 Updated longest transcript for ENSMUSG00000059689: 183 Updated longest transcript for ENSMUSG00000029469: 817 Updated longest transcript for ENSMUSG00000000402: 854 Updated longest transcript for ENSMUSG00000042845: 428 Updated longest transcript for ENSMUSG00000021097: 546 Updated longest transcript for ENSMUSG00000016918: 1594 Updated longest transcript for ENSMUSG00000015289: 362 Updated longest transcript for ENSMUSG00000052270: 649 Updated longest transcript for ENSMUSG00000025489: 278 Updated longest transcript for ENSMUSG00000066129: 1579 Updated longest transcript for ENSMUSG00000046709: 5237 Updated longest transcript for ENSMUSG00000031109: 1796 Updated longest transcript for ENSMUSG00000037447: 2788 Updated longest transcript for ENSMUSG00000094660: 139 Updated longest transcript for ENSMUSG00000043953: 860 Updated longest transcript for ENSMUSG00000032400: 953 Updated longest transcript for ENSMUSG00000079419: 980 Updated longest transcript for ENSMUSG00000044025: 1488 Updated longest transcript for ENSMUSG00000024521: 2192 Updated longest transcript for ENSMUSG00000030711: 278 Updated longest transcript for ENSMUSG00000067001: 682 Updated longest transcript for ENSMUSG00000026938: 175 Updated longest transcript for ENSMUSG00000032525: 2511 Updated longest transcript for ENSMUSG00000096856: 3130 Updated longest transcript for ENSMUSG00000017004: 231 Updated longest transcript for ENSMUSG00000050994: 3387 Updated longest transcript for ENSMUSG00000026447: 2529 Updated longest transcript for ENSMUSG00000037773: 1403 Updated longest transcript for ENSMUSG00000028830: 679 Updated longest transcript for ENSMUSG00000059975: 1451 Updated longest transcript for ENSMUSG00000029826: 649 Updated longest transcript for ENSMUSG00000027248: 988 Updated longest transcript for ENSMUSG00000032556: 217 Updated longest transcript for ENSMUSG00000028445: 468 Updated longest transcript for ENSMUSG00000050830: 2605 Updated longest transcript for ENSMUSG00000090716: 934 Updated longest transcript for ENSMUSG00000110819: 1590 Updated longest transcript for ENSMUSG00000039917: 2998 Updated longest transcript for ENSMUSG00000051242: 468 Updated longest transcript for ENSMUSG00000020220: 2906 Updated longest transcript for ENSMUSG00000026645: 423 Updated longest transcript for ENSMUSG00000026483: 3593 Updated longest transcript for ENSMUSG00000057615: 790 Updated longest transcript for ENSMUSG00000116275: 1672 Updated longest transcript for ENSMUSG00000021461: 1160 Updated longest transcript for ENSMUSG00000030111: 119 Updated longest transcript for ENSMUSG00000068686: 199 Updated longest transcript for ENSMUSG00000039217: 334 Updated longest transcript for ENSMUSG00000036246: 811 Updated longest transcript for ENSMUSG00000074340: 170 Updated longest transcript for ENSMUSG00000048330: 4947 Updated longest transcript for ENSMUSG00000099282: 1290 Updated longest transcript for ENSMUSG00000004364: 2233 Updated longest transcript for ENSMUSG00000002660: 141 Updated longest transcript for ENSMUSG00000026496: 723 Updated longest transcript for ENSMUSG00000051804: 222 Updated longest transcript for ENSMUSG00000092192: 622 Updated longest transcript for ENSMUSG00000000708: 1852 Updated longest transcript for ENSMUSG00000014470: 1022 Updated longest transcript for ENSMUSG00000044106: 3064 Updated longest transcript for ENSMUSG00000048503: 1581 Updated longest transcript for ENSMUSG00000020732: 1496 Updated longest transcript for ENSMUSG00000031264: 400 Updated longest transcript for ENSMUSG00000016933: 2702 Updated longest transcript for ENSMUSG00000034640: 1953 Updated longest transcript for ENSMUSG00000032109: 501 Updated longest transcript for ENSMUSG00000026354: 363 Updated longest transcript for ENSMUSG00000026312: 4259 Updated longest transcript for ENSMUSG00000006311: 18 Updated longest transcript for ENSMUSG00000070530: 706 Updated longest transcript for ENSMUSG00000027887: 3064 Updated longest transcript for ENSMUSG00000053964: 75 Updated longest transcript for ENSMUSG00000029621: 346 Updated longest transcript for ENSMUSG00000021509: 2318 Updated longest transcript for ENSMUSG00000091215: 11 Updated longest transcript for ENSMUSG00000024134: 906 Updated longest transcript for ENSMUSG00000009905: 4443 Updated longest transcript for ENSMUSG00000035493: 923 Updated longest transcript for ENSMUSG00000024309: 42 Updated longest transcript for ENSMUSG00000090061: 1022 Updated longest transcript for ENSMUSG00000018893: 439 Updated longest transcript for ENSMUSG00000076928: 522 Updated longest transcript for ENSMUSG00000034663: 3731 Updated longest transcript for ENSMUSG00000041258: 4675 Updated longest transcript for ENSMUSG00000003444: 636 Updated longest transcript for ENSMUSG00000028974: 1356 Updated longest transcript for ENSMUSG00000024273: 834 Updated longest transcript for ENSMUSG00000019992: 764 Updated longest transcript for ENSMUSG00000072572: 1214 Updated longest transcript for ENSMUSG00000053886: 948 Updated longest transcript for ENSMUSG00000072473: 529 Updated longest transcript for ENSMUSG00000020163: 189 Updated longest transcript for ENSMUSG00000045052: 282 Updated longest transcript for ENSMUSG00000071553: 51 Updated longest transcript for ENSMUSG00000042694: 317 Updated longest transcript for ENSMUSG00000073008: 2581 Updated longest transcript for ENSMUSG00000058124: 594 Updated longest transcript for ENSMUSG00000035296: 2488 Updated longest transcript for ENSMUSG00000019845: 3526 Updated longest transcript for ENSMUSG00000000567: 2250 Updated longest transcript for ENSMUSG00000037224: 1076 Updated longest transcript for ENSMUSG00000020718: 1571 Updated longest transcript for ENSMUSG00000030314: 1589 Updated longest transcript for ENSMUSG00000073956: 4841 Updated longest transcript for ENSMUSG00000020175: 2219 Updated longest transcript for ENSMUSG00000016559: 1446 Updated longest transcript for ENSMUSG00000000439: 4893 Updated longest transcript for ENSMUSG00000021285: 448 Updated longest transcript for ENSMUSG00000027075: 557 Updated longest transcript for ENSMUSG00000025875: 554 Updated longest transcript for ENSMUSG00000078673: 273 Updated longest transcript for ENSMUSG00000016995: 263 Updated longest transcript for ENSMUSG00000033082: 4222 Updated longest transcript for ENSMUSG00000095858: 906 Updated longest transcript for ENSMUSG00000109969: 553 Updated longest transcript for ENSMUSG00000018189: 738 Updated longest transcript for ENSMUSG00000031652: 3548 Updated longest transcript for ENSMUSG00000018924: 391 Updated longest transcript for ENSMUSG00000069899: 461 Updated longest transcript for ENSMUSG00000003199: 188 Updated longest transcript for ENSMUSG00000024063: 2503 Updated longest transcript for ENSMUSG00000004031: 929 Updated longest transcript for ENSMUSG00000026004: 1413 Updated longest transcript for ENSMUSG00000029556: 1327 Updated longest transcript for ENSMUSG00000059851: 810 Updated longest transcript for ENSMUSG00000116069: 586 Updated longest transcript for ENSMUSG00000020660: 174 Updated longest transcript for ENSMUSG00000026004: 1528 Updated longest transcript for ENSMUSG00000039456: 1161 Updated longest transcript for ENSMUSG00000026207: 869 Updated longest transcript for ENSMUSG00000025872: 1237 Updated longest transcript for ENSMUSG00000032878: 3174 Updated longest transcript for ENSMUSG00000074004: 1159 Updated longest transcript for ENSMUSG00000056899: 142 Updated longest transcript for ENSMUSG00000039174: 453 Updated longest transcript for ENSMUSG00000021785: 892 Updated longest transcript for ENSMUSG00000021810: 872 Updated longest transcript for ENSMUSG00000110797: 1693 Updated longest transcript for ENSMUSG00000026828: 513 Updated longest transcript for ENSMUSG00000049225: 2389 Updated longest transcript for ENSMUSG00000043323: 2656 Updated longest transcript for ENSMUSG00000022659: 2726 Updated longest transcript for ENSMUSG00000111273: 2592 Updated longest transcript for ENSMUSG00000020386: 482 Updated longest transcript for ENSMUSG00000050188: 363 Updated longest transcript for ENSMUSG00000095683: 1142 Updated longest transcript for ENSMUSG00000020590: 1681 Updated longest transcript for ENSMUSG00000029121: 925 Updated longest transcript for ENSMUSG00000092541: 596 Updated longest transcript for ENSMUSG00000060427: 557 Updated longest transcript for ENSMUSG00000053868: 182 Updated longest transcript for ENSMUSG00000043572: 724 Updated longest transcript for ENSMUSG00000099689: 2354 Updated longest transcript for ENSMUSG00000008604: 1492 Updated longest transcript for ENSMUSG00000024999: 1417 Updated longest transcript for ENSMUSG00000059970: 503 Updated longest transcript for ENSMUSG00000020836: 983 Updated longest transcript for ENSMUSG00000028668: 2248 Updated longest transcript for ENSMUSG00000066009: 720 Updated longest transcript for ENSMUSG00000021725: 1959 Updated longest transcript for ENSMUSG00000095979: 195 Updated longest transcript for ENSMUSG00000067924: 780 Updated longest transcript for ENSMUSG00000000876: 2071 Updated longest transcript for ENSMUSG00000031214: 4137 Updated longest transcript for ENSMUSG00000022434: 1279 Updated longest transcript for ENSMUSG00000102671: 509 Updated longest transcript for ENSMUSG00000000617: 1548 Updated longest transcript for ENSMUSG00000039715: 188 Updated longest transcript for ENSMUSG00000022546: 402 Updated longest transcript for ENSMUSG00000031271: 1028 Updated longest transcript for ENSMUSG00000082882: 294 Updated longest transcript for ENSMUSG00000026277: 2736 Updated longest transcript for ENSMUSG00000002602: 1032 Updated longest transcript for ENSMUSG00000026750: 141 Updated longest transcript for ENSMUSG00000026753: 2987 Updated longest transcript for ENSMUSG00000071104: 336 Updated longest transcript for ENSMUSG00000026228: 317 Updated longest transcript for ENSMUSG00000035112: 2577 Updated longest transcript for ENSMUSG00000067996: 215 Updated longest transcript for ENSMUSG00000038127: 5951 Updated longest transcript for ENSMUSG00000026943: 77 Updated longest transcript for ENSMUSG00000024451: 544 Updated longest transcript for ENSMUSG00000110195: 3813 Updated longest transcript for ENSMUSG00000034203: 2843 Updated longest transcript for ENSMUSG00000058019: 314 Updated longest transcript for ENSMUSG00000042747: 215 Updated longest transcript for ENSMUSG00000039962: 2594 Updated longest transcript for ENSMUSG00000062995: 567 Updated longest transcript for ENSMUSG00000020892: 376 Updated longest transcript for ENSMUSG00000035413: 614 Updated longest transcript for ENSMUSG00000027433: 452 Updated longest transcript for ENSMUSG00000070733: 2052 Updated longest transcript for ENSMUSG00000031594: 87 Updated longest transcript for ENSMUSG00000023266: 424 Updated longest transcript for ENSMUSG00000035539: 16 Updated longest transcript for ENSMUSG00000001576: 1757 Updated longest transcript for ENSMUSG00000029126: 1480 Updated longest transcript for ENSMUSG00000030787: 1565 Updated longest transcript for ENSMUSG00000034595: 1587 Updated longest transcript for ENSMUSG00000038489: 1436 Updated longest transcript for ENSMUSG00000025917: 434 Updated longest transcript for ENSMUSG00000099032: 3245 Updated longest transcript for ENSMUSG00000061482: 822 Updated longest transcript for ENSMUSG00000005732: 303 Updated longest transcript for ENSMUSG00000025425: 3937 Updated longest transcript for ENSMUSG00000021140: 5339 Updated longest transcript for ENSMUSG00000060985: 278 Updated longest transcript for ENSMUSG00000015750: 1917 Updated longest transcript for ENSMUSG00000030849: 2820 Updated longest transcript for ENSMUSG00000116207: 729 Updated longest transcript for ENSMUSG00000037921: 561 Updated longest transcript for ENSMUSG00000027176: 510 Updated longest transcript for ENSMUSG00000115020: 8731 Updated longest transcript for ENSMUSG00000052026: 1239 Updated longest transcript for ENSMUSG00000021577: 21 Updated longest transcript for ENSMUSG00000090404: 319 Updated longest transcript for ENSMUSG00000035215: 869 Updated longest transcript for ENSMUSG00000025366: 969 Updated longest transcript for ENSMUSG00000022199: 1397 Updated longest transcript for ENSMUSG00000055681: 132 Updated longest transcript for ENSMUSG00000000869: 104 Updated longest transcript for ENSMUSG00000021713: 380 Updated longest transcript for ENSMUSG00000020698: 112 Updated longest transcript for ENSMUSG00000058594: 288 Updated longest transcript for ENSMUSG00000038072: 537 Updated longest transcript for ENSMUSG00000029310: 264 Updated longest transcript for ENSMUSG00000026709: 1115 Updated longest transcript for ENSMUSG00000046111: 536 Updated longest transcript for ENSMUSG00000062278: 343 Updated longest transcript for ENSMUSG00000033883: 4115 Updated longest transcript for ENSMUSG00000042680: 2842 Updated longest transcript for ENSMUSG00000030895: 69 Updated longest transcript for ENSMUSG00000025469: 1825 Updated longest transcript for ENSMUSG00000022416: 2620 Updated longest transcript for ENSMUSG00000090812: 168 Updated longest transcript for ENSMUSG00000017003: 381 Updated longest transcript for ENSMUSG00000023075: 2295 Updated longest transcript for ENSMUSG00000024734: 193 Updated longest transcript for ENSMUSG00000027439: 1901 Updated longest transcript for ENSMUSG00000049491: 1328 Updated longest transcript for ENSMUSG00000002578: 3196 Updated longest transcript for ENSMUSG00000020151: 1106 Updated longest transcript for ENSMUSG00000024677: 1673 Updated longest transcript for ENSMUSG00000054057: 1639 Updated longest transcript for ENSMUSG00000019810: 454 Updated longest transcript for ENSMUSG00000030123: 929 Updated longest transcript for ENSMUSG00000032560: 1035 Updated longest transcript for ENSMUSG00000004319: 2696 Updated longest transcript for ENSMUSG00000030156: 2 Updated longest transcript for ENSMUSG00000094021: 306 Updated longest transcript for ENSMUSG00000038507: 905 Updated longest transcript for ENSMUSG00000030619: 410 Updated longest transcript for ENSMUSG00000055069: 2200 Updated longest transcript for ENSMUSG00000038301: 451 Updated longest transcript for ENSMUSG00000018861: 300 Updated longest transcript for ENSMUSG00000096606: 1412 Updated longest transcript for ENSMUSG00000075602: 385 Updated longest transcript for ENSMUSG00000022554: 753 Updated longest transcript for ENSMUSG00000027517: 103 Updated longest transcript for ENSMUSG00000058904: 253 Updated longest transcript for ENSMUSG00000007670: 409 Updated longest transcript for ENSMUSG00000024018: 6768 Updated longest transcript for ENSMUSG00000029560: 2273 Updated longest transcript for ENSMUSG00000001247: 91 Updated longest transcript for ENSMUSG00000036826: 56 Updated longest transcript for ENSMUSG00000020692: 524 Updated longest transcript for ENSMUSG00000089774: 8260 Updated longest transcript for ENSMUSG00000024028: 160 Updated longest transcript for ENSMUSG00000087376: 91 Updated longest transcript for ENSMUSG00000002550: 1028 Updated longest transcript for ENSMUSG00000047227: 23 Updated longest transcript for ENSMUSG00000034460: 5170 Updated longest transcript for ENSMUSG00000019853: 1636 Updated longest transcript for ENSMUSG00000035842: 2230 Updated longest transcript for ENSMUSG00000035666: 4434 Updated longest transcript for ENSMUSG00000039220: 1023 Updated longest transcript for ENSMUSG00000040952: 613 Updated longest transcript for ENSMUSG00000019139: 92 Updated longest transcript for ENSMUSG00000034551: 7342 Updated longest transcript for ENSMUSG00000073971: 1354 Updated longest transcript for ENSMUSG00000028572: 3076 Updated longest transcript for ENSMUSG00000055730: 487 Updated longest transcript for ENSMUSG00000030924: 1626 Updated longest transcript for ENSMUSG00000029439: 2120 Updated longest transcript for ENSMUSG00000071149: 51 Updated longest transcript for ENSMUSG00000026798: 1811 Updated longest transcript for ENSMUSG00000025194: 3183 Updated longest transcript for ENSMUSG00000035845: 585 Updated longest transcript for ENSMUSG00000019817: 1314 Updated longest transcript for ENSMUSG00000018334: 2745 Updated longest transcript for ENSMUSG00000095578: 196 Updated longest transcript for ENSMUSG00000068566: 2756 Updated longest transcript for ENSMUSG00000038451: 537 Updated longest transcript for ENSMUSG00000025162: 2188 Updated longest transcript for ENSMUSG00000024399: 156 Updated longest transcript for ENSMUSG00000070448: 1337 Updated longest transcript for ENSMUSG00000025231: 2860 Updated longest transcript for ENSMUSG00000107711: 1438 Updated longest transcript for ENSMUSG00000024238: 2401 Updated longest transcript for ENSMUSG00000028681: 552 Updated longest transcript for ENSMUSG00000067847: 84 Updated longest transcript for ENSMUSG00000043613: 285 Updated longest transcript for ENSMUSG00000096744: 22 Updated longest transcript for ENSMUSG00000078626: 11 Updated longest transcript for ENSMUSG00000035910: 971 Updated longest transcript for ENSMUSG00000079173: 371 Updated longest transcript for ENSMUSG00000079402: 1075 Updated longest transcript for ENSMUSG00000108815: 1666 Updated longest transcript for ENSMUSG00000030244: 234 Updated longest transcript for ENSMUSG00000021266: 2627 Updated longest transcript for ENSMUSG00000026176: 1701 Updated longest transcript for ENSMUSG00000038637: 319 Updated longest transcript for ENSMUSG00000057897: 2156 Updated longest transcript for ENSMUSG00000028133: 661 Updated longest transcript for ENSMUSG00000025839: 421 Updated longest transcript for ENSMUSG00000022321: 970 Updated longest transcript for ENSMUSG00000003402: 315 Updated longest transcript for ENSMUSG00000020226: 1194 Updated longest transcript for ENSMUSG00000028517: 1742 Updated longest transcript for ENSMUSG00000032905: 4306 Updated longest transcript for ENSMUSG00000017057: 2353 Updated longest transcript for ENSMUSG00000100296: 80 Updated longest transcript for ENSMUSG00000002222: 4491 Updated longest transcript for ENSMUSG00000021665: 198 Updated longest transcript for ENSMUSG00000009563: 504 Updated longest transcript for ENSMUSG00000116176: 370 Updated longest transcript for ENSMUSG00000062542: 2128 Updated longest transcript for ENSMUSG00000064030: 949 Updated longest transcript for ENSMUSG00000078206: 165 Updated longest transcript for ENSMUSG00000067561: 517 Updated longest transcript for ENSMUSG00000025260: 113 Updated longest transcript for ENSMUSG00000036957: 149 Updated longest transcript for ENSMUSG00000032526: 2109 Updated longest transcript for ENSMUSG00000036501: 3374 Updated longest transcript for ENSMUSG00000108841: 1013 Updated longest transcript for ENSMUSG00000053198: 4701 Updated longest transcript for ENSMUSG00000026348: 1633 Updated longest transcript for ENSMUSG00000041429: 106 Updated longest transcript for ENSMUSG00000094796: 130 Updated longest transcript for ENSMUSG00000033825: 768 Updated longest transcript for ENSMUSG00000051424: 1615 Updated longest transcript for ENSMUSG00000051038: 26 Updated longest transcript for ENSMUSG00000024456: 1740 Updated longest transcript for ENSMUSG00000031600: 4482 Updated longest transcript for ENSMUSG00000059436: 1299 Updated longest transcript for ENSMUSG00000051940: 2120 Updated longest transcript for ENSMUSG00000026080: 4110 Updated longest transcript for ENSMUSG00000028836: 1956 Updated longest transcript for ENSMUSG00000116470: 249 Updated longest transcript for ENSMUSG00000096703: 2190 Updated longest transcript for ENSMUSG00000020777: 1660 Updated longest transcript for ENSMUSG00000025178: 2133 Updated longest transcript for ENSMUSG00000064310: 1182 Updated longest transcript for ENSMUSG00000038773: 1637 Updated longest transcript for ENSMUSG00000053819: 3591 Updated longest transcript for ENSMUSG00000057672: 3629 Updated longest transcript for ENSMUSG00000056019: 4815 Updated longest transcript for ENSMUSG00000027455: 345 Updated longest transcript for ENSMUSG00000031155: 915 Updated longest transcript for ENSMUSG00000041199: 686 Updated longest transcript for ENSMUSG00000006345: 173 Updated longest transcript for ENSMUSG00000022108: 832 Updated longest transcript for ENSMUSG00000028010: 284 Updated longest transcript for ENSMUSG00000022160: 1498 Updated longest transcript for ENSMUSG00000042031: 201 Updated longest transcript for ENSMUSG00000006356: 100 Updated longest transcript for ENSMUSG00000037270: 3346 Updated longest transcript for ENSMUSG00000024986: 867 Updated longest transcript for ENSMUSG00000030314: 1591 Updated longest transcript for ENSMUSG00000017858: 781 Updated longest transcript for ENSMUSG00000001416: 329 Updated longest transcript for ENSMUSG00000027947: 1837 Updated longest transcript for ENSMUSG00000018973: 1117 Updated longest transcript for ENSMUSG00000007216: 2417 Updated longest transcript for ENSMUSG00000024795: 4197 Updated longest transcript for ENSMUSG00000005779: 335 Updated longest transcript for ENSMUSG00000037730: 4496 Updated longest transcript for ENSMUSG00000030557: 3705 Updated longest transcript for ENSMUSG00000033392: 3678 Updated longest transcript for ENSMUSG00000042390: 5405 Updated longest transcript for ENSMUSG00000062661: 3724 Updated longest transcript for ENSMUSG00000046768: 3886 Updated longest transcript for ENSMUSG00000109058: 1423 Updated longest transcript for ENSMUSG00000051498: 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Updated longest transcript for ENSMUSG00000028692: 94 Updated longest transcript for ENSMUSG00000071847: 8901 Updated longest transcript for ENSMUSG00000009646: 498 Updated longest transcript for ENSMUSG00000061825: 219 Updated longest transcript for ENSMUSG00000110103: 301 Updated longest transcript for ENSMUSG00000041915: 1987 Updated longest transcript for ENSMUSG00000025724: 193 Updated longest transcript for ENSMUSG00000039414: 134 Updated longest transcript for ENSMUSG00000074634: 931 Updated longest transcript for ENSMUSG00000030889: 637 Updated longest transcript for ENSMUSG00000078719: 97 Updated longest transcript for ENSMUSG00000068154: 1189 Updated longest transcript for ENSMUSG00000046844: 2326 Updated longest transcript for ENSMUSG00000032978: 185 Updated longest transcript for ENSMUSG00000037017: 382 Updated longest transcript for ENSMUSG00000111214: 1781 Updated longest transcript for ENSMUSG00000073491: 1957 Updated longest transcript for ENSMUSG00000009535: 3497 Updated longest transcript for ENSMUSG00000024617: 3372 Updated longest transcript for ENSMUSG00000007950: 478 Updated longest transcript for ENSMUSG00000028343: 1285 Updated longest transcript for ENSMUSG00000019803: 92 Updated longest transcript for ENSMUSG00000035179: 95 Updated longest transcript for ENSMUSG00000027708: 3515 Updated longest transcript for ENSMUSG00000036966: 1051 Updated longest transcript for ENSMUSG00000089694: 446 Updated longest transcript for ENSMUSG00000030491: 191 Updated longest transcript for ENSMUSG00000035067: 1019 Updated longest transcript for ENSMUSG00000032582: 123 Updated longest transcript for ENSMUSG00000006920: 1823 Updated longest transcript for ENSMUSG00000041248: 1737 Updated longest transcript for ENSMUSG00000034957: 1430 Updated longest transcript for ENSMUSG00000050896: 22 Updated longest transcript for ENSMUSG00000034522: 2600 Updated longest transcript for ENSMUSG00000118215: 17199 Updated longest transcript for ENSMUSG00000090894: 601 Updated longest transcript for ENSMUSG00000074433: 246 Updated longest transcript for ENSMUSG00000091698: 1100 Updated longest transcript for ENSMUSG00000022948: 709 Updated longest transcript for ENSMUSG00000094140: 778 Updated longest transcript for ENSMUSG00000040522: 58 Updated longest transcript for ENSMUSG00000021666: 671 Updated longest transcript for ENSMUSG00000025893: 260 Updated longest transcript for ENSMUSG00000022210: 407 Updated longest transcript for ENSMUSG00000002221: 354 Updated longest transcript for ENSMUSG00000042066: 1191 Updated longest transcript for ENSMUSG00000042105: 1100 Updated longest transcript for ENSMUSG00000020629: 1090 Updated longest transcript for ENSMUSG00000097306: 112 Updated longest transcript for ENSMUSG00000063615: 3325 Updated longest transcript for ENSMUSG00000059743: 55 Updated longest transcript for ENSMUSG00000003228: 3976 Updated longest transcript for ENSMUSG00000037376: 1214 Updated longest transcript for ENSMUSG00000115388: 822 Updated longest transcript for ENSMUSG00000064158: 34 Updated longest transcript for ENSMUSG00000014198: 1289 Updated longest transcript for ENSMUSG00000039738: 4874 Updated longest transcript for ENSMUSG00000022015: 1131 Updated longest transcript for ENSMUSG00000028678: 528 Updated longest transcript for ENSMUSG00000034117: 1242 Updated longest transcript for ENSMUSG00000032065: 528 Updated longest transcript for ENSMUSG00000043411: 2387 Updated longest transcript for ENSMUSG00000033762: 2932 Updated longest transcript for ENSMUSG00000071317: 3170 Updated longest transcript for ENSMUSG00000036372: 118 Updated longest transcript for ENSMUSG00000068252: 975 Updated longest transcript for ENSMUSG00000103421: 1376 Updated longest transcript for ENSMUSG00000066720: 412 Updated longest transcript for ENSMUSG00000021572: 1818 Updated longest transcript for ENSMUSG00000029130: 717 Updated longest transcript for ENSMUSG00000059488: 1193 Updated longest transcript for ENSMUSG00000091206: 125 Updated longest transcript for ENSMUSG00000079093: 179 Updated longest transcript for ENSMUSG00000029405: 2635 Updated longest transcript for ENSMUSG00000046159: 1743 Updated longest transcript for ENSMUSG00000024135: 529 Updated longest transcript for ENSMUSG00000091950: 65 Updated longest transcript for ENSMUSG00000042909: 1858 Updated longest transcript for ENSMUSG00000055960: 1628 Updated longest transcript for ENSMUSG00000098715: 29 Updated longest transcript for ENSMUSG00000045291: 398 Updated longest transcript for ENSMUSG00000015202: 1225 Updated longest transcript for ENSMUSG00000037071: 3464 Updated longest transcript for ENSMUSG00000025418: 1590 Updated longest transcript for ENSMUSG00000033192: 604 Updated longest transcript for ENSMUSG00000063535: 3439 Updated longest transcript for ENSMUSG00000023800: 2398 Updated longest transcript for ENSMUSG00000035125: 103 Updated longest transcript for ENSMUSG00000020808: 704 Updated longest transcript for ENSMUSG00000049097: 806 Updated longest transcript for ENSMUSG00000014418: 2855 Updated longest transcript for ENSMUSG00000049300: 1895 Updated longest transcript for ENSMUSG00000024286: 4252 Updated longest transcript for ENSMUSG00000048234: 1176 Updated longest transcript for ENSMUSG00000063659: 2866 Updated longest transcript for ENSMUSG00000052135: 838 Updated longest transcript for ENSMUSG00000030271: 571 Updated longest transcript for ENSMUSG00000006476: 1346 Updated longest transcript for ENSMUSG00000018930: 302 Updated longest transcript for ENSMUSG00000002393: 356 Updated longest transcript for ENSMUSG00000063576: 536 Updated longest transcript for ENSMUSG00000000560: 3352 Updated longest transcript for ENSMUSG00000041827: 497 Updated longest transcript for ENSMUSG00000113149: 369 Updated longest transcript for ENSMUSG00000030966: 1200 Updated longest transcript for ENSMUSG00000004455: 1465 Updated longest transcript for ENSMUSG00000031756: 586 Updated longest transcript for ENSMUSG00000054381: 161 Updated longest transcript for ENSMUSG00000021891: 2815 Updated longest transcript for ENSMUSG00000058070: 1347 Updated longest transcript for ENSMUSG00000028068: 772 Updated longest transcript for ENSMUSG00000066613: 393 Updated longest transcript for ENSMUSG00000003604: 3679 Updated longest transcript for ENSMUSG00000109745: 129 Updated longest transcript for ENSMUSG00000006179: 581 Updated longest transcript for ENSMUSG00000022986: 1537 Updated longest transcript for ENSMUSG00000020381: 159 Updated longest transcript for ENSMUSG00000050132: 2573 Updated longest transcript for ENSMUSG00000022680: 1666 Updated longest transcript for ENSMUSG00000030614: 1540 Updated longest transcript for ENSMUSG00000021583: 2472 Updated longest transcript for ENSMUSG00000029685: 2388 Updated longest transcript for ENSMUSG00000005069: 1153 Updated longest transcript for ENSMUSG00000074210: 1124 Updated longest transcript for ENSMUSG00000038533: 4176 Updated longest transcript for ENSMUSG00000025326: 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Updated longest transcript for ENSMUSG00000028541: 2877 Updated longest transcript for ENSMUSG00000073956: 5424 Updated longest transcript for ENSMUSG00000045826: 238 Updated longest transcript for ENSMUSG00000050272: 2219 Updated longest transcript for ENSMUSG00000012296: 542 Updated longest transcript for ENSMUSG00000023403: 148 Updated longest transcript for ENSMUSG00000114635: 1254 Updated longest transcript for ENSMUSG00000095706: 156 Updated longest transcript for ENSMUSG00000094237: 78 Updated longest transcript for ENSMUSG00000101158: 196 Updated longest transcript for ENSMUSG00000063058: 22 Updated longest transcript for ENSMUSG00000020376: 205 Updated longest transcript for ENSMUSG00000036214: 9 Updated longest transcript for ENSMUSG00000043631: 1358 Updated longest transcript for ENSMUSG00000068067: 261 Updated longest transcript for ENSMUSG00000029924: 2419 Updated longest transcript for ENSMUSG00000091685: 104 Updated longest transcript for ENSMUSG00000033628: 732 Updated longest transcript for ENSMUSG00000022306: 1016 Updated longest transcript for ENSMUSG00000045658: 1986 Updated longest transcript for ENSMUSG00000030214: 108 Updated longest transcript for ENSMUSG00000078676: 1588 Updated longest transcript for ENSMUSG00000091155: 133 Updated longest transcript for ENSMUSG00000095934: 151 Updated longest transcript for ENSMUSG00000041323: 738 Updated longest transcript for ENSMUSG00000038383: 293 Updated longest transcript for ENSMUSG00000074863: 2076 Updated longest transcript for ENSMUSG00000031934: 4010 Updated longest transcript for ENSMUSG00000046282: 166 Updated longest transcript for ENSMUSG00000031534: 697 Updated longest transcript for ENSMUSG00000007041: 258 Updated longest transcript for ENSMUSG00000020328: 804 Updated longest transcript for ENSMUSG00000094613: 906 Updated longest transcript for ENSMUSG00000019699: 3249 Updated longest transcript for ENSMUSG00000078958: 974 Updated longest transcript for ENSMUSG00000021215: 2052 Updated longest transcript for ENSMUSG00000035392: 1383 Updated longest transcript for ENSMUSG00000095852: 357 Updated longest transcript for ENSMUSG00000079427: 151 Updated longest transcript for ENSMUSG00000039826: 3515 Updated longest transcript for ENSMUSG00000043832: 363 Updated longest transcript for ENSMUSG00000050714: 3499 Updated longest transcript for ENSMUSG00000031870: 287 Updated longest transcript for ENSMUSG00000050957: 91 Updated longest transcript for ENSMUSG00000022440: 1829 Updated longest transcript for ENSMUSG00000069729: 4569 Updated longest transcript for ENSMUSG00000052675: 716 Updated longest transcript for ENSMUSG00000031870: 3473 Updated longest transcript for ENSMUSG00000020166: 2739 Updated longest transcript for ENSMUSG00000025170: 912 Updated longest transcript for ENSMUSG00000032307: 1538 Updated longest transcript for ENSMUSG00000029026: 2742 Updated longest transcript for ENSMUSG00000036078: 861 Updated longest transcript for ENSMUSG00000118578: 68 Updated longest transcript for ENSMUSG00000094822: 3620 Updated longest transcript for ENSMUSG00000095716: 370 Updated longest transcript for ENSMUSG00000038936: 512 Updated longest transcript for ENSMUSG00000060204: 811 Updated longest transcript for ENSMUSG00000039795: 783 Updated longest transcript for ENSMUSG00000020331: 462 Updated longest transcript for ENSMUSG00000007338: 1214 Updated longest transcript for ENSMUSG00000073878: 393 Updated longest transcript for ENSMUSG00000028975: 806 Updated longest transcript for ENSMUSG00000070802: 1750 Updated longest transcript for ENSMUSG00000038525: 1076 Updated longest transcript for ENSMUSG00000047867: 2822 Updated longest transcript for ENSMUSG00000043385: 2075 Updated longest transcript for ENSMUSG00000025613: 662 Updated longest transcript for ENSMUSG00000020684: 2473 Updated longest transcript for ENSMUSG00000071714: 1810 Updated longest transcript for ENSMUSG00000004933: 1783 Updated longest transcript for ENSMUSG00000031079: 2412 Updated longest transcript for ENSMUSG00000026479: 710 Updated longest transcript for ENSMUSG00000025838: 89 Updated longest transcript for ENSMUSG00000113450: 1399 Updated longest transcript for ENSMUSG00000022292: 3293 Updated longest transcript for ENSMUSG00000026925: 1590 Updated longest transcript for ENSMUSG00000001566: 835 Updated longest transcript for ENSMUSG00000037788: 4047 Updated longest transcript for ENSMUSG00000027556: 320 Updated longest transcript for ENSMUSG00000041237: 1021 Updated longest transcript for ENSMUSG00000055027: 1811 Updated longest transcript for ENSMUSG00000073514: 8711 Updated longest transcript for ENSMUSG00000041698: 1058 Updated longest transcript for ENSMUSG00000020952: 539 Updated longest transcript for ENSMUSG00000095294: 1291 Updated longest transcript for ENSMUSG00000024666: 747 Updated longest transcript for ENSMUSG00000020541: 621 Updated longest transcript for ENSMUSG00000027249: 113 Updated longest transcript for ENSMUSG00000024911: 57 Updated longest transcript for ENSMUSG00000006342: 640 Updated longest transcript for ENSMUSG00000029790: 119 Updated longest transcript for ENSMUSG00000024566: 3139 Updated longest transcript for ENSMUSG00000028717: 1744 Updated longest transcript for ENSMUSG00000022149: 533 Updated longest transcript for ENSMUSG00000056812: 7815 Updated longest transcript for ENSMUSG00000039382: 478 Updated longest transcript for ENSMUSG00000015176: 1360 Updated longest transcript for ENSMUSG00000033628: 1578 Updated longest transcript for ENSMUSG00000089687: 65 Updated longest transcript for ENSMUSG00000061046: 272 Updated longest transcript for ENSMUSG00000037761: 321 Updated longest transcript for ENSMUSG00000042607: 1975 Updated longest transcript for ENSMUSG00000002500: 87 Updated longest transcript for ENSMUSG00000031207: 1918 Updated longest transcript for ENSMUSG00000029191: 1211 Updated longest transcript for ENSMUSG00000050705: 176 Updated longest transcript for ENSMUSG00000078249: 1207 Updated longest transcript for ENSMUSG00000052852: 2384 Updated longest transcript for ENSMUSG00000029291: 2035 Updated longest transcript for ENSMUSG00000024666: 912 Updated longest transcript for ENSMUSG00000041068: 478 Updated longest transcript for ENSMUSG00000059486: 1354 Updated longest transcript for ENSMUSG00000057836: 835 Updated longest transcript for ENSMUSG00000024981: 1364 Updated longest transcript for ENSMUSG00000049506: 422 Updated longest transcript for ENSMUSG00000024517: 326 Updated longest transcript for ENSMUSG00000039728: 69 Updated longest transcript for ENSMUSG00000025127: 287 Updated longest transcript for ENSMUSG00000031497: 958 Updated longest transcript for ENSMUSG00000049588: 746 Updated longest transcript for ENSMUSG00000051153: 29 Updated longest transcript for ENSMUSG00000022586: 380 Updated longest transcript for ENSMUSG00000040380: 4067 Updated longest transcript for ENSMUSG00000021838: 4356 Updated longest transcript for ENSMUSG00000060519: 3177 Updated longest transcript for ENSMUSG00000053654: 183 Updated longest transcript for ENSMUSG00000027867: 824 Updated longest transcript for ENSMUSG00000062433: 284 Updated longest transcript for ENSMUSG00000024589: 4942 Updated longest transcript for ENSMUSG00000032352: 1299 Updated longest transcript for ENSMUSG00000032046: 608 Updated longest transcript for ENSMUSG00000024943: 93 Updated longest transcript for ENSMUSG00000026728: 342 Updated longest transcript for ENSMUSG00000091945: 5542 Updated longest transcript for ENSMUSG00000055923: 89 Updated longest transcript for ENSMUSG00000079092: 110 Updated longest transcript for ENSMUSG00000039701: 911 Updated longest transcript for ENSMUSG00000000916: 100 Updated longest transcript for ENSMUSG00000015879: 1032 Updated longest transcript for ENSMUSG00000081225: 70 Updated longest transcript for ENSMUSG00000050074: 214 Updated longest transcript for ENSMUSG00000021139: 1632 Updated longest transcript for ENSMUSG00000046345: 624 Updated longest 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longest transcript for ENSMUSG00000038286: 619 Updated longest transcript for ENSMUSG00000078657: 237 Updated longest transcript for ENSMUSG00000089953: 1393 Updated longest transcript for ENSMUSG00000028020: 2675 Updated longest transcript for ENSMUSG00000041915: 1988 Updated longest transcript for ENSMUSG00000027833: 870 Updated longest transcript for ENSMUSG00000039710: 243 Updated longest transcript for ENSMUSG00000031635: 700 Updated longest transcript for ENSMUSG00000058354: 557 Updated longest transcript for ENSMUSG00000024287: 1873 Updated longest transcript for ENSMUSG00000027086: 230 Updated longest transcript for ENSMUSG00000039509: 2251 Updated longest transcript for ENSMUSG00000027452: 1508 Updated longest transcript for ENSMUSG00000035228: 994 Updated longest transcript for ENSMUSG00000023235: 835 Updated longest transcript for ENSMUSG00000063796: 1558 Updated longest transcript for ENSMUSG00000039701: 1969 Updated longest transcript for ENSMUSG00000043372: 811 Updated longest transcript for ENSMUSG00000042444: 6147 Updated longest transcript for ENSMUSG00000054675: 1167 Updated longest transcript for ENSMUSG00000025332: 694 Updated longest transcript for ENSMUSG00000068205: 3074 Updated longest transcript for ENSMUSG00000060477: 1246 Updated longest transcript for ENSMUSG00000095567: 497 Updated longest transcript for ENSMUSG00000049350: 128 Updated longest transcript for ENSMUSG00000039168: 151 Updated longest transcript for ENSMUSG00000023830: 1304 Updated longest transcript for ENSMUSG00000037600: 865 Updated longest transcript for ENSMUSG00000059554: 448 Updated longest transcript for ENSMUSG00000022837: 887 Updated longest transcript for ENSMUSG00000040852: 2329 Updated longest transcript for ENSMUSG00000043943: 1483 Updated longest transcript for ENSMUSG00000053490: 888 Updated longest transcript for ENSMUSG00000045322: 273 Updated longest transcript for ENSMUSG00000031156: 1606 Updated longest transcript for ENSMUSG00000061838: 2040 Updated longest transcript for ENSMUSG00000021919: 542 Updated longest transcript for ENSMUSG00000017950: 2819 Updated longest transcript for ENSMUSG00000014232: 230 Updated longest transcript for ENSMUSG00000021108: 2895 Updated longest transcript for ENSMUSG00000031508: 4068 Updated longest transcript for ENSMUSG00000043441: 1459 Updated longest transcript for ENSMUSG00000044086: 311 Updated longest transcript for ENSMUSG00000066368: 358 Updated longest transcript for ENSMUSG00000090774: 9776 Updated longest transcript for ENSMUSG00000093752: 4494 Updated longest transcript for ENSMUSG00000057554: 1430 Updated longest transcript for ENSMUSG00000107039: 1498 Updated longest transcript for ENSMUSG00000091415: 1246 Updated longest transcript for ENSMUSG00000032125: 2835 Updated longest transcript for ENSMUSG00000046671: 995 Updated longest transcript for ENSMUSG00000007670: 1642 Updated longest transcript for ENSMUSG00000001120: 633 Updated longest transcript for ENSMUSG00000041536: 290 Updated longest transcript for ENSMUSG00000022053: 2796 Updated longest transcript for ENSMUSG00000030159: 12 Updated longest transcript for ENSMUSG00000033323: 681 Updated longest transcript for ENSMUSG00000034919: 728 Updated longest transcript for ENSMUSG00000024052: 2846 Updated longest transcript for ENSMUSG00000030706: 3557 Updated longest transcript for ENSMUSG00000045404: 789 Updated longest transcript for ENSMUSG00000028431: 1972 Updated longest transcript for ENSMUSG00000028850: 411 Updated longest transcript for ENSMUSG00000034868: 1191 Updated longest transcript for ENSMUSG00000026103: 3544 Updated longest transcript for ENSMUSG00000102742: 1744 Updated longest transcript for ENSMUSG00000010067: 1379 Updated longest transcript for ENSMUSG00000042286: 134 Updated longest transcript for ENSMUSG00000019826: 1284 Updated longest transcript for ENSMUSG00000024403: 969 Updated longest transcript for ENSMUSG00000048766: 307 Updated longest transcript for ENSMUSG00000025007: 891 Updated longest transcript for ENSMUSG00000019146: 3401 Updated longest transcript for ENSMUSG00000025817: 3697 Updated longest transcript for ENSMUSG00000041845: 822 Updated longest transcript for ENSMUSG00000037095: 264 Updated longest transcript for ENSMUSG00000104250: 514 Updated longest transcript for ENSMUSG00000024818: 775 Updated longest transcript for ENSMUSG00000074480: 4215 Updated longest transcript for ENSMUSG00000052713: 3720 Updated longest transcript for ENSMUSG00000073257: 103 Updated longest transcript for ENSMUSG00000032263: 280 Updated longest transcript for ENSMUSG00000015452: 299 Updated longest transcript for ENSMUSG00000048776: 459 Updated longest transcript for ENSMUSG00000001910: 2563 Updated longest transcript for ENSMUSG00000097395: 366 Updated longest transcript for ENSMUSG00000021457: 2997 Updated longest transcript for ENSMUSG00000110086: 609 Updated longest transcript for ENSMUSG00000078942: 2428 Updated longest transcript for ENSMUSG00000020863: 1979 Updated longest transcript for ENSMUSG00000019803: 1401 Updated longest transcript for ENSMUSG00000089960: 573 Updated longest transcript for ENSMUSG00000089929: 147 Updated longest transcript for ENSMUSG00000092470: 1861 Updated longest transcript for ENSMUSG00000053747: 742 Updated longest transcript for ENSMUSG00000048455: 297 Updated longest transcript for ENSMUSG00000024231: 2331 Updated longest transcript for ENSMUSG00000029598: 2389 Updated longest transcript for ENSMUSG00000026439: 1867 Updated longest transcript for ENSMUSG00000039904: 1538 Updated longest transcript for ENSMUSG00000021947: 456 Updated longest transcript for ENSMUSG00000054408: 2830 Updated longest transcript for ENSMUSG00000021832: 1004 Updated longest transcript for ENSMUSG00000032399: 222 Updated longest transcript for ENSMUSG00000074892: 3764 Updated longest transcript for ENSMUSG00000050996: 669 Updated longest transcript for ENSMUSG00000034401: 818 Updated longest transcript for ENSMUSG00000024300: 1333 Updated longest transcript for ENSMUSG00000070565: 5529 Updated longest transcript for ENSMUSG00000056536: 3582 Updated longest transcript for ENSMUSG00000002635: 182 Updated longest transcript for ENSMUSG00000031799: 1318 Updated longest transcript for ENSMUSG00000001506: 1423 Updated longest transcript for ENSMUSG00000030584: 1037 Updated longest transcript for ENSMUSG00000047420: 857 Updated longest transcript for ENSMUSG00000035762: 1710 Updated longest transcript for ENSMUSG00000052981: 1210 Updated longest transcript for ENSMUSG00000027200: 2461 Updated longest transcript for ENSMUSG00000078812: 639 Updated longest transcript for ENSMUSG00000004609: 4646 Updated longest transcript for ENSMUSG00000013150: 209 Updated longest transcript for ENSMUSG00000111314: 867 Updated longest transcript for ENSMUSG00000011752: 885 Updated longest transcript for ENSMUSG00000024331: 3121 Updated longest transcript for ENSMUSG00000036853: 1208 Updated longest transcript for ENSMUSG00000026956: 1758 Updated longest transcript for ENSMUSG00000111842: 161 Updated longest transcript for ENSMUSG00000045474: 229 Updated longest transcript for ENSMUSG00000071065: 2403 Updated longest transcript for ENSMUSG00000021898: 240 Updated longest transcript for ENSMUSG00000064145: 2140 Updated longest transcript for ENSMUSG00000015083: 159 Updated longest transcript for ENSMUSG00000026609: 37 Updated longest transcript for ENSMUSG00000039512: 492 Updated longest transcript for ENSMUSG00000039640: 631 Updated longest transcript for ENSMUSG00000035403: 540 Updated longest transcript for ENSMUSG00000051111: 126 Updated longest transcript for ENSMUSG00000052833: 787 Updated longest transcript for ENSMUSG00000008590: 892 Updated longest transcript for ENSMUSG00000021884: 1182 Updated longest transcript for ENSMUSG00000060201: 219 Updated longest transcript for ENSMUSG00000090363: 1188 Updated longest transcript for ENSMUSG00000026975: 548 Updated longest transcript for ENSMUSG00000032187: 1249 Updated longest transcript for ENSMUSG00000052435: 179 Updated longest transcript for ENSMUSG00000015749: 2140 Updated longest transcript for ENSMUSG00000040564: 113 Updated longest transcript for ENSMUSG00000073557: 573 Updated longest transcript for ENSMUSG00000035946: 587 Updated longest transcript for ENSMUSG00000025789: 4060 Updated longest transcript for ENSMUSG00000038594: 42 Updated longest transcript for ENSMUSG00000097075: 407 Updated longest transcript for ENSMUSG00000028593: 118 Updated longest transcript for ENSMUSG00000027570: 769 Updated longest transcript for ENSMUSG00000095887: 105 Updated longest transcript for ENSMUSG00000029730: 136 Updated longest transcript for ENSMUSG00000019843: 1409 Updated longest transcript for ENSMUSG00000007783: 127 Updated longest transcript for ENSMUSG00000096534: 290 Updated longest transcript for ENSMUSG00000028114: 2910 Updated longest transcript for ENSMUSG00000095518: 761 Updated longest transcript for ENSMUSG00000032555: 512 Updated longest transcript for ENSMUSG00000030446: 739 Updated longest transcript for ENSMUSG00000079362: 2398 Updated longest transcript for ENSMUSG00000038175: 1512 Updated longest transcript for ENSMUSG00000021408: 4365 Updated longest transcript for ENSMUSG00000029575: 71 Updated longest transcript for ENSMUSG00000029419: 1557 Updated longest transcript for ENSMUSG00000068732: 2453 Updated longest transcript for ENSMUSG00000027751: 1982 Updated longest transcript for ENSMUSG00000028382: 5015 Updated longest transcript for ENSMUSG00000067591: 315 Updated longest transcript for ENSMUSG00000030753: 1159 Updated longest transcript for ENSMUSG00000023073: 2937 Updated longest transcript for ENSMUSG00000031826: 790 Updated longest transcript for ENSMUSG00000050788: 1470 Updated longest transcript for ENSMUSG00000038132: 1778 Updated longest transcript for ENSMUSG00000028329: 310 Updated longest transcript for ENSMUSG00000094447: 125 Updated longest transcript for ENSMUSG00000000531: 740 Updated longest transcript for ENSMUSG00000052276: 817 Updated longest transcript for ENSMUSG00000057378: 381 Updated longest transcript for ENSMUSG00000078350: 3124 Updated longest transcript for ENSMUSG00000035125: 2693 Updated longest transcript for ENSMUSG00000024446: 174 Updated longest transcript for ENSMUSG00000005198: 413 Updated longest transcript for ENSMUSG00000038122: 3292 Updated longest transcript for ENSMUSG00000025420: 372 Updated longest transcript for ENSMUSG00000090966: 5165 Updated longest transcript for ENSMUSG00000013846: 372 Updated longest transcript for ENSMUSG00000037337: 743 Updated longest transcript for ENSMUSG00000071855: 1105 Updated longest transcript for ENSMUSG00000038085: 188 Updated longest transcript for ENSMUSG00000031666: 1171 Updated longest transcript for ENSMUSG00000068037: 481 Updated longest transcript for ENSMUSG00000111709: 105 Updated longest transcript for ENSMUSG00000026027: 1795 Updated longest transcript for ENSMUSG00000002297: 239 Updated longest transcript for ENSMUSG00000096273: 241 Updated longest transcript for ENSMUSG00000022790: 1964 Updated longest transcript for ENSMUSG00000033420: 4309 Updated longest transcript for ENSMUSG00000023015: 268 Updated longest transcript for ENSMUSG00000016257: 747 Updated longest transcript for ENSMUSG00000056579: 439 Updated longest transcript for ENSMUSG00000056978: 136 Updated longest transcript for ENSMUSG00000038990: 1503 Updated longest transcript for ENSMUSG00000059495: 5340 Updated longest transcript for ENSMUSG00000010914: 1402 Updated longest transcript for ENSMUSG00000116053: 1481 Updated longest transcript for ENSMUSG00000093639: 1606 Updated longest transcript for ENSMUSG00000031497: 3631 Updated longest transcript for ENSMUSG00000033916: 213 Updated longest transcript for ENSMUSG00000063457: 34 Updated longest transcript for ENSMUSG00000038295: 906 Updated longest transcript for ENSMUSG00000026107: 3606 Updated longest transcript for ENSMUSG00000098377: 1001 Updated longest transcript for ENSMUSG00000058192: 10938 Updated longest transcript for ENSMUSG00000028068: 1602 Updated longest transcript for ENSMUSG00000036411: 451 Updated longest transcript for ENSMUSG00000051228: 1974 Updated longest transcript for ENSMUSG00000111393: 1711 Updated longest transcript for ENSMUSG00000022240: 389 Updated longest transcript for ENSMUSG00000062868: 2093 Updated longest transcript for ENSMUSG00000000561: 4033 Updated longest transcript for ENSMUSG00000059022: 1596 Updated longest transcript for ENSMUSG00000021763: 1001 Updated longest transcript for ENSMUSG00000001119: 767 Updated longest transcript for ENSMUSG00000052331: 2935 Updated longest transcript for ENSMUSG00000093456: 478 Updated longest transcript for ENSMUSG00000030317: 347 Updated longest transcript for ENSMUSG00000021270: 935 Updated longest transcript for ENSMUSG00000036437: 1604 Updated longest transcript for ENSMUSG00000033282: 4383 Updated longest transcript for ENSMUSG00000075202: 2684 Updated longest transcript for ENSMUSG00000046845: 51 Updated longest transcript for ENSMUSG00000101819: 120 Updated longest transcript for ENSMUSG00000024404: 2103 Updated longest transcript for ENSMUSG00000018362: 1531 Updated longest transcript for ENSMUSG00000032280: 1781 Updated longest transcript for ENSMUSG00000049152: 612 Updated longest transcript for ENSMUSG00000060572: 404 Updated longest transcript for ENSMUSG00000053846: 1995 Updated longest transcript for ENSMUSG00000033972: 797 Updated longest transcript for ENSMUSG00000116418: 99 Updated longest transcript for ENSMUSG00000026036: 2131 Updated longest transcript for ENSMUSG00000032079: 1185 Updated longest transcript for ENSMUSG00000079444: 989 Updated longest transcript for ENSMUSG00000066896: 1771 Updated longest transcript for ENSMUSG00000054976: 5023 Updated longest transcript for ENSMUSG00000096574: 1133 Updated longest transcript for ENSMUSG00000040613: 1015 Updated longest transcript for ENSMUSG00000039196: 113 Updated longest transcript for ENSMUSG00000055471: 470 Updated longest transcript for ENSMUSG00000096629: 1133 Updated longest transcript for ENSMUSG00000027208: 1624 Updated longest transcript for ENSMUSG00000015659: 1918 Updated longest transcript for ENSMUSG00000022768: 2134 Updated longest transcript for ENSMUSG00000032641: 186 Updated longest transcript for ENSMUSG00000094103: 3125 Updated longest transcript for ENSMUSG00000036144: 1157 Updated longest transcript for ENSMUSG00000019782: 172 Updated longest transcript for ENSMUSG00000032705: 724 Updated longest transcript for ENSMUSG00000041959: 239 Updated longest transcript for ENSMUSG00000025417: 1997 Updated longest transcript for ENSMUSG00000050197: 106 Updated longest transcript for ENSMUSG00000092164: 241 Updated longest transcript for ENSMUSG00000039976: 4523 Updated longest transcript for ENSMUSG00000029138: 258 Updated longest transcript for ENSMUSG00000061533: 1323 Updated longest transcript for ENSMUSG00000028923: 1052 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longest transcript for ENSMUSG00000034028: 3148 Updated longest transcript for ENSMUSG00000041324: 4492 Updated longest transcript for ENSMUSG00000049985: 2804 Updated longest transcript for ENSMUSG00000014769: 303 Updated longest transcript for ENSMUSG00000060503: 3177 Updated longest transcript for ENSMUSG00000059218: 71 Updated longest transcript for ENSMUSG00000069588: 1874 Updated longest transcript for ENSMUSG00000059142: 3905 Updated longest transcript for ENSMUSG00000033308: 1267 Updated longest transcript for ENSMUSG00000028573: 652 Updated longest transcript for ENSMUSG00000058881: 3982 Updated longest transcript for ENSMUSG00000029248: 210 Updated longest transcript for ENSMUSG00000046598: 1807 Updated longest transcript for ENSMUSG00000042213: 2673 Updated longest transcript for ENSMUSG00000060057: 1755 Updated longest transcript for ENSMUSG00000022369: 1282 Updated longest transcript for ENSMUSG00000032596: 2608 Updated longest transcript for ENSMUSG00000026623: 5721 Updated longest transcript for ENSMUSG00000113267: 225 Updated longest transcript for ENSMUSG00000073418: 576 Updated longest transcript for ENSMUSG00000054387: 3616 Updated longest transcript for ENSMUSG00000034574: 2445 Updated longest transcript for ENSMUSG00000001313: 547 Updated longest transcript for ENSMUSG00000045302: 4831 Updated longest transcript for ENSMUSG00000016503: 321 Updated longest transcript for ENSMUSG00000057098: 3343 Updated longest transcript for ENSMUSG00000028102: 676 Updated longest transcript for ENSMUSG00000031402: 934 Updated longest transcript for ENSMUSG00000041671: 1255 Updated longest transcript for ENSMUSG00000032198: 1694 Updated longest transcript for ENSMUSG00000095464: 203 Updated longest transcript for ENSMUSG00000055571: 2522 Updated longest transcript for ENSMUSG00000003269: 1124 Updated longest transcript for ENSMUSG00000034297: 3953 Updated longest transcript for ENSMUSG00000040466: 267 Updated longest transcript for ENSMUSG00000030683: 514 Updated longest transcript for ENSMUSG00000074182: 138 Updated longest transcript for ENSMUSG00000095677: 329 Updated longest transcript for ENSMUSG00000041747: 2284 Updated longest transcript for ENSMUSG00000046441: 1123 Updated longest transcript for ENSMUSG00000042599: 6588 Updated longest transcript for ENSMUSG00000021182: 1287 Updated longest transcript for ENSMUSG00000037573: 767 Updated longest transcript for ENSMUSG00000042385: 236 Updated longest transcript for ENSMUSG00000038092: 373 Updated longest transcript for ENSMUSG00000072726: 319 Updated longest transcript for ENSMUSG00000060579: 293 Updated longest transcript for ENSMUSG00000060187: 521 Updated longest transcript for ENSMUSG00000073116: 2095 Updated longest transcript for ENSMUSG00000020334: 487 Updated longest transcript for ENSMUSG00000031481: 2141 Updated longest transcript for ENSMUSG00000047369: 224 Updated longest transcript for ENSMUSG00000024604: 857 Updated longest transcript for ENSMUSG00000040738: 297 Updated longest transcript for ENSMUSG00000091080: 22 Updated longest transcript for ENSMUSG00000079323: 738 Updated longest transcript for ENSMUSG00000040220: 1965 Updated longest transcript for ENSMUSG00000091407: 7261 Updated longest transcript for ENSMUSG00000024436: 288 Updated longest transcript for ENSMUSG00000015668: 485 Updated longest transcript for ENSMUSG00000027699: 3283 Updated longest transcript for ENSMUSG00000063386: 3385 Updated longest transcript for ENSMUSG00000029111: 809 Updated longest transcript for ENSMUSG00000051355: 191 Updated longest transcript for ENSMUSG00000022488: 3332 Updated longest transcript for ENSMUSG00000024165: 2021 Updated longest transcript for ENSMUSG00000020396: 591 Updated longest transcript for ENSMUSG00000020690: 143 Updated longest transcript for ENSMUSG00000071014: 188 Updated longest transcript for ENSMUSG00000026784: 659 Updated longest transcript for ENSMUSG00000042870: 717 Updated longest transcript for ENSMUSG00000038893: 913 Updated longest transcript for ENSMUSG00000085933: 2924 Updated longest transcript for ENSMUSG00000052534: 5190 Updated longest transcript for ENSMUSG00000027886: 76 Updated longest transcript for ENSMUSG00000001962: 181 Updated longest transcript for ENSMUSG00000054716: 186 Updated longest transcript for ENSMUSG00000020847: 1181 Updated longest transcript for ENSMUSG00000046417: 831 Updated longest transcript for ENSMUSG00000050296: 1021 Updated longest transcript for ENSMUSG00000021131: 420 Updated longest transcript for ENSMUSG00000027692: 7876 Updated longest transcript for ENSMUSG00000019303: 577 Updated longest transcript for ENSMUSG00000028879: 1965 Updated longest transcript for ENSMUSG00000042448: 753 Updated longest transcript for ENSMUSG00000018537: 1164 Updated longest transcript for ENSMUSG00000056494: 2540 Updated longest transcript for ENSMUSG00000020307: 335 Updated longest transcript for ENSMUSG00000031730: 600 Updated longest transcript for ENSMUSG00000033210: 3093 Updated longest transcript for ENSMUSG00000006546: 753 Updated longest transcript for ENSMUSG00000044405: 258 Updated longest transcript for ENSMUSG00000026234: 5901 Updated longest transcript for ENSMUSG00000102030: 1634 Updated longest transcript for ENSMUSG00000032112: 569 Updated longest transcript for ENSMUSG00000024588: 5169 Updated longest transcript for ENSMUSG00000096852: 544 Updated longest transcript for ENSMUSG00000028538: 893 Updated longest transcript for ENSMUSG00000032312: 675 Updated longest transcript for ENSMUSG00000017311: 166 Updated longest transcript for ENSMUSG00000020224: 1210 Updated longest transcript for ENSMUSG00000035236: 1915 Updated longest transcript for ENSMUSG00000030048: 180 Updated longest transcript for ENSMUSG00000078355: 372 Updated longest transcript for ENSMUSG00000034120: 1068 Updated longest transcript for ENSMUSG00000055341: 312 Updated longest transcript for ENSMUSG00000071158: 1 Updated longest transcript for ENSMUSG00000019478: 611 Updated longest transcript for ENSMUSG00000095493: 421 Updated longest transcript for ENSMUSG00000056008: 1095 Updated longest transcript for ENSMUSG00000035420: 906 Updated longest transcript for ENSMUSG00000026251: 1308 Updated longest transcript for ENSMUSG00000017707: 2169 Updated longest transcript for ENSMUSG00000021991: 802 Updated longest transcript for ENSMUSG00000059042: 48 Updated longest transcript for ENSMUSG00000048307: 1589 Updated longest transcript for ENSMUSG00000091376: 149 Updated longest transcript for ENSMUSG00000027882: 1290 Updated longest transcript for ENSMUSG00000069911: 2735 Updated longest transcript for ENSMUSG00000024552: 792 Updated longest transcript for ENSMUSG00000096530: 123 Updated longest transcript for ENSMUSG00000010097: 2660 Updated longest transcript for ENSMUSG00000006675: 603 Updated longest transcript for ENSMUSG00000036120: 2587 Updated longest transcript for ENSMUSG00000038147: 2918 Updated longest transcript for ENSMUSG00000034252: 4134 Updated longest transcript for ENSMUSG00000020776: 1098 Updated longest transcript for ENSMUSG00000043505: 786 Updated longest transcript for ENSMUSG00000026615: 312 Updated longest transcript for ENSMUSG00000036315: 172 Updated longest transcript for ENSMUSG00000096555: 127 Updated longest transcript for ENSMUSG00000066936: 171 Updated longest transcript for ENSMUSG00000040435: 77 Updated longest transcript for ENSMUSG00000027714: 418 Updated longest transcript for ENSMUSG00000057058: 1291 Updated longest transcript for ENSMUSG00000094800: 1004 Updated longest transcript for ENSMUSG00000049858: 623 Updated longest transcript for ENSMUSG00000044528: 2459 Updated longest transcript for ENSMUSG00000039725: 1123 Updated longest transcript for ENSMUSG00000039745: 449 Updated longest transcript for ENSMUSG00000024493: 1200 Updated longest transcript for ENSMUSG00000034042: 924 Updated longest transcript for ENSMUSG00000027206: 1889 Updated longest transcript for ENSMUSG00000025279: 3226 Updated longest transcript for ENSMUSG00000022757: 439 Updated longest transcript for ENSMUSG00000063932: 136 Updated longest transcript for ENSMUSG00000020133: 1094 Updated longest transcript for ENSMUSG00000060397: 1997 Updated longest transcript for ENSMUSG00000063730: 2251 Updated longest transcript for ENSMUSG00000043542: 2288 Updated longest transcript for ENSMUSG00000031758: 729 Updated longest transcript for ENSMUSG00000096994: 328 Updated longest transcript for ENSMUSG00000090935: 4085 Updated longest transcript for ENSMUSG00000016498: 669 Updated longest transcript for ENSMUSG00000037447: 3826 Updated longest transcript for ENSMUSG00000048489: 479 Updated longest transcript for ENSMUSG00000093839: 3980 Updated longest transcript for ENSMUSG00000056270: 777 Updated longest transcript for ENSMUSG00000025576: 1477 Updated longest transcript for ENSMUSG00000050705: 330 Updated longest transcript for ENSMUSG00000026039: 1100 Updated longest transcript for ENSMUSG00000028756: 547 Updated longest transcript for ENSMUSG00000032558: 2130 Updated longest transcript for ENSMUSG00000117477: 230 Updated longest transcript for ENSMUSG00000020621: 407 Updated longest transcript for ENSMUSG00000024901: 1647 Updated longest transcript for ENSMUSG00000014769: 1281 Updated longest transcript for ENSMUSG00000053641: 2572 Updated longest transcript for ENSMUSG00000025103: 1494 Updated longest transcript for ENSMUSG00000024502: 578 Updated longest transcript for ENSMUSG00000041396: 43 Updated longest transcript for ENSMUSG00000025227: 963 Updated longest transcript for ENSMUSG00000021115: 1197 Updated longest transcript for ENSMUSG00000015467: 188 Updated longest transcript for ENSMUSG00000063052: 2281 Updated longest transcript for ENSMUSG00000046879: 556 Updated longest transcript for ENSMUSG00000047394: 246 Updated longest transcript for ENSMUSG00000037942: 935 Updated longest transcript for ENSMUSG00000032041: 3360 Updated longest transcript for ENSMUSG00000034755: 4263 Updated longest transcript for ENSMUSG00000004667: 359 Updated longest transcript for ENSMUSG00000096839: 1098 Updated longest transcript for ENSMUSG00000044024: 440 Updated longest transcript for ENSMUSG00000022105: 1709 Updated longest transcript for ENSMUSG00000048758: 119 Updated longest transcript for ENSMUSG00000040151: 4782 Updated longest transcript for ENSMUSG00000093904: 4348 Updated longest transcript for ENSMUSG00000034949: 689 Updated longest transcript for ENSMUSG00000020522: 3571 Updated longest transcript for ENSMUSG00000006490: 156 Updated longest transcript for ENSMUSG00000032633: 1202 Updated longest transcript for ENSMUSG00000057903: 2438 Updated longest transcript for ENSMUSG00000037583: 300 Updated longest transcript for ENSMUSG00000015994: 726 Updated longest transcript for ENSMUSG00000070426: 1235 Updated longest transcript for ENSMUSG00000022857: 792 Updated longest transcript for ENSMUSG00000050505: 2261 Updated longest transcript for ENSMUSG00000020747: 1019 Updated longest transcript for ENSMUSG00000117975: 1905 Updated longest transcript for ENSMUSG00000036858: 564 Updated longest transcript for ENSMUSG00000025199: 1176 Updated longest transcript for ENSMUSG00000078765: 752 Updated longest transcript for ENSMUSG00000101645: 1453 Updated longest transcript for ENSMUSG00000097436: 1851 Updated longest transcript for ENSMUSG00000037993: 388 Updated longest transcript for ENSMUSG00000053470: 663 Updated longest transcript for ENSMUSG00000028018: 1331 Updated longest transcript for ENSMUSG00000020668: 3609 Updated longest transcript for ENSMUSG00000034607: 2005 Updated longest transcript for ENSMUSG00000004591: 2639 Updated longest transcript for ENSMUSG00000051457: 2444 Updated longest transcript for ENSMUSG00000030519: 736 Updated longest transcript for ENSMUSG00000028580: 1661 Updated longest transcript for ENSMUSG00000052396: 721 Updated longest transcript for ENSMUSG00000046722: 2174 Updated longest transcript for ENSMUSG00000029152: 720 Updated longest transcript for ENSMUSG00000070304: 3766 Updated longest transcript for ENSMUSG00000030854: 1030 Updated longest transcript for ENSMUSG00000064254: 150 Updated longest transcript for ENSMUSG00000020279: 2440 Updated longest transcript for ENSMUSG00000049894: 3277 Updated longest transcript for ENSMUSG00000060073: 1952 Updated longest transcript for ENSMUSG00000020354: 1710 Updated longest transcript for ENSMUSG00000023845: 8339 Updated longest transcript for ENSMUSG00000048001: 696 Updated longest transcript for ENSMUSG00000025511: 474 Updated longest transcript for ENSMUSG00000018196: 2762 Updated longest transcript for ENSMUSG00000020709: 99 Updated longest transcript for ENSMUSG00000028247: 1523 Updated longest transcript for ENSMUSG00000047766: 2856 Updated longest transcript for ENSMUSG00000033021: 172 Updated longest transcript for ENSMUSG00000061798: 5039 Updated longest transcript for ENSMUSG00000009115: 415 Updated longest transcript for ENSMUSG00000061477: 262 Updated longest transcript for ENSMUSG00000011148: 350 Updated longest transcript for ENSMUSG00000042042: 1650 Updated longest transcript for ENSMUSG00000036940: 308 Updated longest transcript for ENSMUSG00000053253: 1415 Updated longest transcript for ENSMUSG00000001156: 3829 Updated longest transcript for ENSMUSG00000023224: 522 Updated longest transcript for ENSMUSG00000084927: 404 Updated longest transcript for ENSMUSG00000075143: 3467 Updated longest transcript for ENSMUSG00000033022: 209 Updated longest transcript for ENSMUSG00000026579: 753 Updated longest transcript for ENSMUSG00000046182: 2721 Updated longest transcript for ENSMUSG00000019088: 352 Updated longest transcript for ENSMUSG00000033417: 4351 Updated longest transcript for ENSMUSG00000025465: 602 Updated longest transcript for ENSMUSG00000052446: 2083 Updated longest transcript for ENSMUSG00000055430: 1264 Updated longest transcript for ENSMUSG00000050625: 781 Updated longest transcript for ENSMUSG00000022146: 1794 Updated longest transcript for ENSMUSG00000021198: 452 Updated longest transcript for ENSMUSG00000037379: 541 Updated longest transcript for ENSMUSG00000029190: 1328 Updated longest transcript for ENSMUSG00000026626: 248 Updated longest transcript for ENSMUSG00000031851: 1053 Updated longest transcript for ENSMUSG00000021553: 2385 Updated longest transcript for ENSMUSG00000028415: 93 Updated longest transcript for ENSMUSG00000095368: 306 Updated longest transcript for ENSMUSG00000061118: 952 Updated longest transcript for ENSMUSG00000062906: 411 Updated longest transcript for ENSMUSG00000034785: 416 Updated longest transcript for ENSMUSG00000035164: 4062 Updated longest transcript for ENSMUSG00000034452: 1560 Updated longest transcript for ENSMUSG00000006711: 2222 Updated longest transcript for ENSMUSG00000053040: 4300 Updated longest transcript for ENSMUSG00000034908: 1397 Updated longest transcript for ENSMUSG00000033161: 337 Updated longest transcript for ENSMUSG00000066154: 399 Updated longest transcript for ENSMUSG00000019817: 2590 Updated longest transcript for ENSMUSG00000029998: 2707 Updated longest transcript for ENSMUSG00000026919: 201 Updated longest transcript for ENSMUSG00000039058: 1133 Updated longest transcript for ENSMUSG00000031004: 305 Updated longest transcript for ENSMUSG00000027376: 2141 Updated longest transcript for ENSMUSG00000090059: 2328 Updated longest transcript for ENSMUSG00000029550: 1593 Updated longest transcript for ENSMUSG00000103749: 1744 Updated longest transcript for ENSMUSG00000004113: 2676 Updated longest transcript for ENSMUSG00000062329: 525 Updated longest transcript for ENSMUSG00000073139: 1295 Updated longest transcript for ENSMUSG00000026443: 614 Updated longest transcript for ENSMUSG00000075486: 246 Updated longest transcript for ENSMUSG00000024735: 4427 Updated longest transcript for ENSMUSG00000058063: 497 Updated longest transcript for ENSMUSG00000027833: 1868 Updated longest transcript for ENSMUSG00000064293: 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Updated longest transcript for ENSMUSG00000030717: 480 Updated longest transcript for ENSMUSG00000046110: 1894 Updated longest transcript for ENSMUSG00000034880: 284 Updated longest transcript for ENSMUSG00000069823: 2066 Updated longest transcript for ENSMUSG00000096788: 161 Updated longest transcript for ENSMUSG00000031493: 72 Updated longest transcript for ENSMUSG00000047832: 1164 Updated longest transcript for ENSMUSG00000070867: 4969 Updated longest transcript for ENSMUSG00000095253: 3895 Updated longest transcript for ENSMUSG00000027774: 1679 Updated longest transcript for ENSMUSG00000046410: 3110 Updated longest transcript for ENSMUSG00000106893: 17 Updated longest transcript for ENSMUSG00000030729: 6438 Updated longest transcript for ENSMUSG00000056116: 1328 Updated longest transcript for ENSMUSG00000031509: 681 Updated longest transcript for ENSMUSG00000067878: 237 Updated longest transcript for ENSMUSG00000019907: 2691 Updated longest transcript for ENSMUSG00000034255: 1363 Updated longest transcript for ENSMUSG00000026356: 527 Updated longest transcript for ENSMUSG00000031910: 2369 Updated longest transcript for ENSMUSG00000028034: 4423 Updated longest transcript for ENSMUSG00000035168: 2026 Updated longest transcript for ENSMUSG00000018841: 3215 Updated longest transcript for ENSMUSG00000042759: 786 Updated longest transcript for ENSMUSG00000000690: 641 Updated longest transcript for ENSMUSG00000015812: 148 Updated longest transcript for ENSMUSG00000063240: 62 Updated longest transcript for ENSMUSG00000012126: 836 Updated longest transcript for ENSMUSG00000028597: 444 Updated longest transcript for ENSMUSG00000020063: 3035 Updated longest transcript for ENSMUSG00000029445: 171 Updated longest transcript for ENSMUSG00000047986: 230 Updated longest transcript for ENSMUSG00000005553: 465 Updated longest transcript for ENSMUSG00000100032: 196 Updated longest transcript for ENSMUSG00000109336: 2067 Updated longest transcript for ENSMUSG00000026430: 2294 Updated longest transcript for ENSMUSG00000029096: 683 Updated longest transcript for ENSMUSG00000045364: 63 Updated longest transcript for ENSMUSG00000005893: 5424 Updated longest transcript for ENSMUSG00000014778: 234 Updated longest transcript for ENSMUSG00000044024: 768 Updated longest transcript for ENSMUSG00000116086: 424 Updated longest transcript for ENSMUSG00000025716: 83 Updated longest transcript for ENSMUSG00000030498: 2461 Updated longest transcript for ENSMUSG00000060224: 1925 Updated longest transcript for ENSMUSG00000026787: 3528 Updated longest transcript for ENSMUSG00000071662: 264 Updated longest transcript for ENSMUSG00000070520: 5027 Updated longest transcript for ENSMUSG00000025917: 686 Updated longest transcript for ENSMUSG00000027010: 4092 Updated longest transcript for ENSMUSG00000003660: 239 Updated longest transcript for ENSMUSG00000058589: 6259 Updated longest transcript for ENSMUSG00000017978: 1100 Updated longest transcript for ENSMUSG00000029601: 133 Updated longest transcript for ENSMUSG00000044534: 1377 Updated longest transcript for ENSMUSG00000028419: 658 Updated longest transcript for ENSMUSG00000031285: 2840 Updated longest transcript for ENSMUSG00000034059: 1042 Updated longest transcript for ENSMUSG00000018900: 1267 Updated longest transcript for ENSMUSG00000036299: 809 Updated longest transcript for ENSMUSG00000029167: 3930 Updated longest transcript for ENSMUSG00000089917: 758 Updated longest transcript for ENSMUSG00000022696: 1457 Updated longest transcript for ENSMUSG00000116024: 918 Updated longest transcript for ENSMUSG00000094543: 763 Updated longest transcript for ENSMUSG00000024970: 208 Updated longest transcript for ENSMUSG00000056679: 2756 Updated longest transcript for ENSMUSG00000047298: 3256 Updated longest transcript for ENSMUSG00000000751: 3139 Updated longest transcript for ENSMUSG00000039728: 4432 Updated longest transcript for ENSMUSG00000017679: 3703 Updated longest transcript for ENSMUSG00000022742: 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Updated longest transcript for ENSMUSG00000008540: 494 Updated longest transcript for ENSMUSG00000023391: 1112 Updated longest transcript for ENSMUSG00000026986: 683 Updated longest transcript for ENSMUSG00000032123: 1704 Updated longest transcript for ENSMUSG00000022095: 1749 Updated longest transcript for ENSMUSG00000032034: 2873 Updated longest transcript for ENSMUSG00000036968: 1017 Updated longest transcript for ENSMUSG00000022779: 1325 Updated longest transcript for ENSMUSG00000040586: 983 Updated longest transcript for ENSMUSG00000021552: 216 Updated longest transcript for ENSMUSG00000033565: 5172 Updated longest transcript for ENSMUSG00000037260: 681 Updated longest transcript for ENSMUSG00000046658: 3512 Updated longest transcript for ENSMUSG00000042207: 3911 Updated longest transcript for ENSMUSG00000041653: 121 Updated longest transcript for ENSMUSG00000018476: 1318 Updated longest transcript for ENSMUSG00000020649: 914 Updated longest transcript for ENSMUSG00000036775: 1650 Updated longest transcript for ENSMUSG00000023341: 331 Updated longest transcript for ENSMUSG00000026305: 1251 Updated longest transcript for ENSMUSG00000048500: 71 Updated longest transcript for ENSMUSG00000099711: 361 Updated longest transcript for ENSMUSG00000018974: 2052 Updated longest transcript for ENSMUSG00000031411: 142 Updated longest transcript for ENSMUSG00000028454: 274 Updated longest transcript for ENSMUSG00000024266: 206 Updated longest transcript for ENSMUSG00000056515: 2677 Updated longest transcript for ENSMUSG00000072066: 935 Updated longest transcript for ENSMUSG00000044213: 536 Updated longest transcript for ENSMUSG00000022992: 1764 Updated longest transcript for ENSMUSG00000074947: 4009 Updated longest transcript for ENSMUSG00000027444: 110 Updated longest transcript for ENSMUSG00000046909: 640 Updated longest transcript for ENSMUSG00000021133: 752 Updated longest transcript for ENSMUSG00000078588: 367 Updated longest transcript for ENSMUSG00000033453: 2860 Updated longest transcript for ENSMUSG00000004035: 557 Updated longest transcript for ENSMUSG00000063801: 5159 Updated longest transcript for ENSMUSG00000003762: 425 Updated longest transcript for ENSMUSG00000085111: 771 Updated longest transcript for ENSMUSG00000052819: 359 Updated longest transcript for ENSMUSG00000026896: 2111 Updated longest transcript for ENSMUSG00000024873: 663 Updated longest transcript for ENSMUSG00000076937: 140 Updated longest transcript for ENSMUSG00000021577: 833 Updated longest transcript for ENSMUSG00000025234: 5036 Updated longest transcript for ENSMUSG00000072955: 457 Updated longest transcript for ENSMUSG00000036598: 67 Updated longest transcript for ENSMUSG00000010476: 2650 Updated longest transcript for ENSMUSG00000025816: 848 Updated longest transcript for ENSMUSG00000009092: 622 Updated longest transcript for ENSMUSG00000021668: 1480 Updated longest transcript for ENSMUSG00000037465: 1492 Updated longest transcript for ENSMUSG00000030145: 1577 Updated longest transcript for ENSMUSG00000050240: 4262 Updated longest transcript for ENSMUSG00000031730: 3518 Updated longest transcript for ENSMUSG00000015759: 955 Updated longest transcript for ENSMUSG00000061751: 1418 Updated longest transcript for ENSMUSG00000050103: 678 Updated longest transcript for ENSMUSG00000050014: 1152 Updated longest transcript for ENSMUSG00000018263: 1732 Updated longest transcript for ENSMUSG00000079685: 5189 Updated longest transcript for ENSMUSG00000046367: 1288 Updated longest transcript for ENSMUSG00000031778: 1871 Updated longest transcript for ENSMUSG00000055561: 1660 Updated longest transcript for ENSMUSG00000054142: 6381 Updated longest transcript for ENSMUSG00000024067: 281 Updated longest transcript for ENSMUSG00000023009: 664 Updated longest transcript for ENSMUSG00000094464: 3794 Updated longest transcript for ENSMUSG00000096788: 179 Updated longest transcript for ENSMUSG00000066108: 514 Updated longest transcript for ENSMUSG00000030559: 205 Updated longest transcript for ENSMUSG00000024668: 3447 Updated longest transcript for ENSMUSG00000075605: 233 Updated longest transcript for ENSMUSG00000070460: 2294 Updated longest transcript for ENSMUSG00000027770: 2549 Updated longest transcript for ENSMUSG00000042642: 1227 Updated longest transcript for ENSMUSG00000025571: 2588 Updated longest transcript for ENSMUSG00000024907: 180 Updated longest transcript for ENSMUSG00000031549: 1149 Updated longest transcript for ENSMUSG00000020333: 3555 Updated longest transcript for ENSMUSG00000029596: 172 Updated longest transcript for ENSMUSG00000031504: 479 Updated longest transcript for ENSMUSG00000015846: 3269 Updated longest transcript for ENSMUSG00000007458: 1230 Updated longest transcript for ENSMUSG00000028832: 277 Updated longest transcript for ENSMUSG00000021285: 885 Updated longest transcript for ENSMUSG00000059463: 1428 Updated longest transcript for ENSMUSG00000048833: 3632 Updated longest transcript for ENSMUSG00000019909: 76 Updated longest transcript for ENSMUSG00000050840: 1096 Updated longest transcript for ENSMUSG00000022494: 2146 Updated longest transcript for ENSMUSG00000032482: 6091 Updated longest transcript for ENSMUSG00000020882: 1393 Updated longest transcript for ENSMUSG00000030200: 997 Updated longest transcript for ENSMUSG00000056383: 1114 Updated longest transcript for ENSMUSG00000056895: 213 Updated longest transcript for ENSMUSG00000034825: 3119 Updated longest transcript for ENSMUSG00000062563: 1294 Updated longest transcript for ENSMUSG00000023015: 892 Updated longest transcript for ENSMUSG00000025529: 1506 Updated longest transcript for ENSMUSG00000030789: 463 Updated longest transcript for ENSMUSG00000014668: 2983 Updated longest transcript for ENSMUSG00000097886: 285 Updated longest transcript for ENSMUSG00000071203: 868 Updated longest transcript for ENSMUSG00000037426: 3154 Updated longest transcript for ENSMUSG00000028434: 1379 Updated longest transcript for ENSMUSG00000039608: 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Updated longest transcript for ENSMUSG00000070923: 1857 Updated longest transcript for ENSMUSG00000041583: 236 Updated longest transcript for ENSMUSG00000004821: 62 Updated longest transcript for ENSMUSG00000019756: 991 Updated longest transcript for ENSMUSG00000073008: 3178 Updated longest transcript for ENSMUSG00000062753: 313 Updated longest transcript for ENSMUSG00000020580: 2214 Updated longest transcript for ENSMUSG00000003411: 2511 Updated longest transcript for ENSMUSG00000083947: 1129 Updated longest transcript for ENSMUSG00000089997: 71 Updated longest transcript for ENSMUSG00000073497: 984 Updated longest transcript for ENSMUSG00000037035: 1857 Updated longest transcript for ENSMUSG00000019312: 368 Updated longest transcript for ENSMUSG00000037482: 2157 Updated longest transcript for ENSMUSG00000040260: 99 Updated longest transcript for ENSMUSG00000041974: 406 Updated longest transcript for ENSMUSG00000062621: 2464 Updated longest transcript for ENSMUSG00000030911: 1075 Updated longest transcript for ENSMUSG00000047562: 249 Updated longest transcript for ENSMUSG00000069743: 1811 Updated longest transcript for ENSMUSG00000041881: 289 Updated longest transcript for ENSMUSG00000036928: 454 Updated longest transcript for ENSMUSG00000049100: 4285 Updated longest transcript for ENSMUSG00000047841: 274 Updated longest transcript for ENSMUSG00000019432: 218 Updated longest transcript for ENSMUSG00000038301: 1708 Updated longest transcript for ENSMUSG00000022122: 2440 Updated longest transcript for ENSMUSG00000062054: 1395 Updated longest transcript for ENSMUSG00000079038: 295 Updated longest transcript for ENSMUSG00000026204: 522 Updated longest transcript for ENSMUSG00000050266: 1018 Updated longest transcript for ENSMUSG00000073650: 248 Updated longest transcript for ENSMUSG00000025666: 3242 Updated longest transcript for ENSMUSG00000018547: 3702 Updated longest transcript for ENSMUSG00000039270: 6015 Updated longest transcript for ENSMUSG00000035566: 5445 Updated longest transcript for ENSMUSG00000059030: 2307 Updated longest transcript for ENSMUSG00000049608: 2556 Updated longest transcript for ENSMUSG00000073234: 499 Updated longest transcript for ENSMUSG00000078941: 1020 Updated longest transcript for ENSMUSG00000031734: 397 Updated longest transcript for ENSMUSG00000066900: 1253 Updated longest transcript for ENSMUSG00000025355: 1724 Updated longest transcript for ENSMUSG00000063888: 1841 Updated longest transcript for ENSMUSG00000069237: 2400 Updated longest transcript for ENSMUSG00000025223: 544 Updated longest transcript for ENSMUSG00000029433: 2098 Updated longest transcript for ENSMUSG00000019846: 155 Updated longest transcript for ENSMUSG00000032409: 157 Updated longest transcript for ENSMUSG00000020474: 1168 Updated longest transcript for ENSMUSG00000019891: 1382 Updated longest transcript for ENSMUSG00000024480: 801 Updated longest transcript for ENSMUSG00000039163: 706 Updated longest transcript for ENSMUSG00000070821: 2199 Updated longest transcript for ENSMUSG00000072791: 24 Updated longest transcript for ENSMUSG00000035629: 2265 Updated longest transcript for ENSMUSG00000022323: 458 Updated longest transcript for ENSMUSG00000030525: 532 Updated longest transcript for ENSMUSG00000062683: 311 Updated longest transcript for ENSMUSG00000069744: 59 Updated longest transcript for ENSMUSG00000029161: 348 Updated longest transcript for ENSMUSG00000107662: 1511 Updated longest transcript for ENSMUSG00000041124: 1556 Updated longest transcript for ENSMUSG00000037443: 1421 Updated longest transcript for ENSMUSG00000020926: 2116 Updated longest transcript for ENSMUSG00000078566: 1079 Updated longest transcript for ENSMUSG00000039656: 868 Updated longest transcript for ENSMUSG00000060131: 1837 Updated longest transcript for ENSMUSG00000021294: 1115 Updated longest transcript for ENSMUSG00000040521: 687 Updated longest transcript for ENSMUSG00000028719: 2378 Updated longest transcript for ENSMUSG00000048621: 1713 Updated longest transcript for ENSMUSG00000035354: 2869 Updated longest transcript for ENSMUSG00000071648: 278 Updated longest transcript for ENSMUSG00000037722: 2005 Updated longest transcript for ENSMUSG00000019818: 2207 Updated longest transcript for ENSMUSG00000062372: 1001 Updated longest transcript for ENSMUSG00000063681: 2000 Updated longest transcript for ENSMUSG00000012017: 709 Updated longest transcript for ENSMUSG00000096409: 4294 Updated longest transcript for ENSMUSG00000004105: 1431 Updated longest transcript for ENSMUSG00000032845: 542 Updated longest transcript for ENSMUSG00000000320: 1072 Updated longest transcript for ENSMUSG00000104267: 119 Updated longest transcript for ENSMUSG00000001473: 373 Updated longest transcript for ENSMUSG00000029152: 2595 Updated longest transcript for ENSMUSG00000032612: 1208 Updated longest transcript for ENSMUSG00000030051: 151 Updated longest transcript for ENSMUSG00000028633: 791 Updated longest transcript for ENSMUSG00000028255: 882 Updated longest transcript for ENSMUSG00000005686: 1495 Updated longest transcript for ENSMUSG00000095063: 123 Updated longest transcript for ENSMUSG00000036941: 3796 Updated longest transcript for ENSMUSG00000096806: 2698 Updated longest transcript for ENSMUSG00000005836: 1153 Updated longest transcript for ENSMUSG00000045929: 1865 Updated longest transcript for ENSMUSG00000057789: 1504 Updated longest transcript for ENSMUSG00000096923: 1201 Updated longest transcript for ENSMUSG00000021666: 714 Updated longest transcript for ENSMUSG00000034789: 265 Updated longest transcript for ENSMUSG00000033712: 735 Updated longest transcript for ENSMUSG00000071893: 694 Updated longest transcript for ENSMUSG00000029851: 1348 Updated longest transcript for ENSMUSG00000025971: 2219 Updated longest transcript for ENSMUSG00000037243: 105 Updated longest transcript for ENSMUSG00000052974: 263 Updated longest transcript for ENSMUSG00000033126: 3666 Updated longest transcript for ENSMUSG00000016524: 492 Updated longest transcript for ENSMUSG00000000149: 2141 Updated longest transcript for ENSMUSG00000026387: 252 Updated longest transcript for ENSMUSG00000045034: 1870 Updated longest transcript for ENSMUSG00000037196: 306 Updated longest transcript for ENSMUSG00000028840: 1500 Updated longest transcript for ENSMUSG00000048497: 1155 Updated longest transcript for ENSMUSG00000024013: 1449 Updated longest transcript for ENSMUSG00000096872: 762 Updated longest transcript for ENSMUSG00000052584: 575 Updated longest transcript for ENSMUSG00000056553: 517 Updated longest transcript for ENSMUSG00000039195: 2367 Updated longest transcript for ENSMUSG00000109713: 367 Updated longest transcript for ENSMUSG00000042807: 6346 Updated longest transcript for ENSMUSG00000010529: 559 Updated longest transcript for ENSMUSG00000034758: 225 Updated longest transcript for ENSMUSG00000021136: 1849 Updated longest transcript for ENSMUSG00000074797: 533 Updated longest transcript for ENSMUSG00000038510: 495 Updated longest transcript for ENSMUSG00000054967: 480 Updated longest transcript for ENSMUSG00000038550: 131 Updated longest transcript for ENSMUSG00000059864: 1684 Updated longest transcript for ENSMUSG00000050028: 616 Updated longest transcript for ENSMUSG00000020773: 249 Updated longest transcript for ENSMUSG00000040312: 181 Updated longest transcript for ENSMUSG00000020263: 874 Updated longest transcript for ENSMUSG00000109324: 911 Updated longest transcript for ENSMUSG00000022844: 146 Updated longest transcript for ENSMUSG00000027490: 1281 Updated longest transcript for ENSMUSG00000028886: 3406 Updated longest transcript for ENSMUSG00000005251: 1152 Updated longest transcript for ENSMUSG00000035403: 2431 Updated longest transcript for ENSMUSG00000037437: 236 Updated longest transcript for ENSMUSG00000062103: 2453 Updated longest transcript for ENSMUSG00000043013: 2183 Updated longest transcript for ENSMUSG00000074673: 1140 Updated longest transcript for ENSMUSG00000021313: 1414 Updated longest transcript for ENSMUSG00000047284: 1100 Updated longest transcript for ENSMUSG00000115463: 172 Updated longest transcript for ENSMUSG00000021096: 6078 Updated longest transcript for ENSMUSG00000036964: 2093 Updated longest transcript for ENSMUSG00000057329: 1942 Updated longest transcript for ENSMUSG00000046493: 3182 Updated longest transcript for ENSMUSG00000022179: 618 Updated longest transcript for ENSMUSG00000026617: 1151 Updated longest transcript for ENSMUSG00000040367: 50 Updated longest transcript for ENSMUSG00000020542: 1884 Updated longest transcript for ENSMUSG00000021364: 2795 Updated longest transcript for ENSMUSG00000064280: 331 Updated longest transcript for ENSMUSG00000041536: 378 Updated longest transcript for ENSMUSG00000056605: 388 Updated longest transcript for ENSMUSG00000028996: 300 Updated longest transcript for ENSMUSG00000054766: 1635 Updated longest transcript for ENSMUSG00000036499: 3314 Updated longest transcript for ENSMUSG00000107268: 1079 Updated longest transcript for ENSMUSG00000028630: 3949 Updated longest transcript for ENSMUSG00000036570: 257 Updated longest transcript for ENSMUSG00000090824: 1294 Updated longest transcript for ENSMUSG00000095430: 5120 Updated longest transcript for ENSMUSG00000072878: 242 Updated longest transcript for ENSMUSG00000025916: 413 Updated longest transcript for ENSMUSG00000043079: 1876 Updated longest transcript for ENSMUSG00000025969: 3325 Updated longest transcript for ENSMUSG00000038599: 1283 Updated longest transcript for ENSMUSG00000039714: 294 Updated longest transcript for ENSMUSG00000027834: 2744 Updated longest transcript for ENSMUSG00000035873: 486 Updated longest transcript for ENSMUSG00000040204: 1663 Updated longest transcript for ENSMUSG00000041135: 1609 Updated longest transcript for ENSMUSG00000039220: 1025 Updated longest transcript for ENSMUSG00000063975: 2027 Updated longest transcript for ENSMUSG00000027378: 184 Updated longest transcript for ENSMUSG00000034990: 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Updated longest transcript for ENSMUSG00000029203: 1079 Updated longest transcript for ENSMUSG00000034247: 2607 Updated longest transcript for ENSMUSG00000044367: 927 Updated longest transcript for ENSMUSG00000032511: 2170 Updated longest transcript for ENSMUSG00000055555: 584 Updated longest transcript for ENSMUSG00000090690: 717 Updated longest transcript for ENSMUSG00000049241: 1835 Updated longest transcript for ENSMUSG00000030483: 310 Updated longest transcript for ENSMUSG00000035640: 2549 Updated longest transcript for ENSMUSG00000006281: 4222 Updated longest transcript for ENSMUSG00000029486: 700 Updated longest transcript for ENSMUSG00000032493: 754 Updated longest transcript for ENSMUSG00000099418: 229 Updated longest transcript for ENSMUSG00000022673: 780 Updated longest transcript for ENSMUSG00000032002: 292 Updated longest transcript for ENSMUSG00000020891: 1158 Updated longest transcript for ENSMUSG00000045733: 2586 Updated longest transcript for ENSMUSG00000116201: 178 Updated longest transcript for ENSMUSG00000049456: 1989 Updated longest transcript for ENSMUSG00000034994: 416 Updated longest transcript for ENSMUSG00000063239: 1976 Updated longest transcript for ENSMUSG00000039304: 3998 Updated longest transcript for ENSMUSG00000078240: 117 Updated longest transcript for ENSMUSG00000036960: 607 Updated longest transcript for ENSMUSG00000094269: 896 Updated longest transcript for ENSMUSG00000001248: 1120 Updated longest transcript for ENSMUSG00000057722: 2302 Updated longest transcript for ENSMUSG00000032186: 8468 Updated longest transcript for ENSMUSG00000028293: 849 Updated longest transcript for ENSMUSG00000028737: 1420 Updated longest transcript for ENSMUSG00000018425: 1276 Updated longest transcript for ENSMUSG00000029467: 811 Updated longest transcript for ENSMUSG00000041617: 453 Updated longest transcript for ENSMUSG00000118537: 412 Updated longest transcript for ENSMUSG00000031631: 2876 Updated longest transcript for ENSMUSG00000074182: 271 Updated longest transcript for ENSMUSG00000000378: 381 Updated longest transcript for ENSMUSG00000032300: 845 Updated longest transcript for ENSMUSG00000061751: 6443 Updated longest transcript for ENSMUSG00000038965: 2658 Updated longest transcript for ENSMUSG00000042419: 215 Updated longest transcript for ENSMUSG00000062753: 378 Updated longest transcript for ENSMUSG00000022389: 3182 Updated longest transcript for ENSMUSG00000021114: 2744 Updated longest transcript for ENSMUSG00000060935: 591 Updated longest transcript for ENSMUSG00000116153: 3029 Updated longest transcript for ENSMUSG00000049357: 486 Updated longest transcript for ENSMUSG00000029286: 1366 Updated longest transcript for ENSMUSG00000014786: 268 Updated longest transcript for ENSMUSG00000035799: 700 Updated longest transcript for ENSMUSG00000058194: 800 Updated longest transcript for ENSMUSG00000114456: 1258 Updated longest transcript for ENSMUSG00000032405: 2639 Updated longest transcript for ENSMUSG00000029414: 192 Updated longest transcript for ENSMUSG00000042810: 3155 Updated longest transcript for ENSMUSG00000112856: 22 Updated longest transcript for ENSMUSG00000022119: 794 Updated longest transcript for ENSMUSG00000049658: 4375 Updated longest transcript for ENSMUSG00000022435: 150 Updated longest transcript for ENSMUSG00000029190: 2139 Updated longest transcript for ENSMUSG00000051936: 92 Updated longest transcript for ENSMUSG00000040632: 1037 Updated longest transcript for ENSMUSG00000098835: 1455 Updated longest transcript for ENSMUSG00000020067: 1062 Updated longest transcript for ENSMUSG00000094080: 2239 Updated longest transcript for ENSMUSG00000041287: 106 Updated longest transcript for ENSMUSG00000036013: 276 Updated longest transcript for ENSMUSG00000032024: 2709 Updated longest transcript for ENSMUSG00000044170: 2749 Updated longest transcript for ENSMUSG00000039194: 689 Updated longest transcript for ENSMUSG00000067909: 142 Updated longest transcript for ENSMUSG00000075572: 159 Updated longest transcript for ENSMUSG00000002031: 2867 Updated longest transcript for ENSMUSG00000040213: 2737 Updated longest transcript for ENSMUSG00000028172: 2137 Updated longest transcript for ENSMUSG00000021843: 3800 Updated longest transcript for ENSMUSG00000026511: 952 Updated longest transcript for ENSMUSG00000038517: 1624 Updated longest transcript for ENSMUSG00000077450: 5184 Updated longest transcript for ENSMUSG00000049560: 339 Updated longest transcript for ENSMUSG00000020513: 215 Updated longest transcript for ENSMUSG00000030432: 559 Updated longest transcript for ENSMUSG00000020883: 7035 Updated longest transcript for ENSMUSG00000048481: 605 Updated longest transcript for ENSMUSG00000027333: 1042 Updated longest transcript for ENSMUSG00000091101: 22 Updated longest transcript for ENSMUSG00000028838: 1276 Updated longest transcript for ENSMUSG00000024818: 1122 Updated longest transcript for ENSMUSG00000029312: 1794 Updated longest transcript for ENSMUSG00000056947: 928 Updated longest transcript for ENSMUSG00000039070: 1451 Updated longest transcript for ENSMUSG00000003469: 1497 Updated longest transcript for ENSMUSG00000025323: 3214 Updated longest transcript for ENSMUSG00000095041: 2545 Updated longest transcript for ENSMUSG00000022401: 4451 Updated longest transcript for ENSMUSG00000040297: 2252 Updated longest transcript for ENSMUSG00000025512: 2911 Updated longest transcript for ENSMUSG00000079357: 2637 Updated longest transcript for ENSMUSG00000061136: 4474 Updated longest transcript for ENSMUSG00000055799: 982 Updated longest transcript for ENSMUSG00000037216: 242 Updated longest transcript for ENSMUSG00000024146: 768 Updated longest transcript for ENSMUSG00000022971: 1622 Updated longest transcript for ENSMUSG00000031330: 485 Updated longest transcript for ENSMUSG00000000134: 2598 Updated longest transcript for ENSMUSG00000031781: 3293 Updated longest transcript for ENSMUSG00000034126: 2327 Updated longest transcript for ENSMUSG00000017781: 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Updated longest transcript for ENSMUSG00000009585: 1437 Updated longest transcript for ENSMUSG00000024309: 177 Updated longest transcript for ENSMUSG00000073436: 4161 Updated longest transcript for ENSMUSG00000026087: 100 Updated longest transcript for ENSMUSG00000030125: 331 Updated longest transcript for ENSMUSG00000042564: 2933 Updated longest transcript for ENSMUSG00000038331: 2797 Updated longest transcript for ENSMUSG00000019917: 460 Updated longest transcript for ENSMUSG00000061780: 69 Updated longest transcript for ENSMUSG00000030500: 1765 Updated longest transcript for ENSMUSG00000039787: 892 Updated longest transcript for ENSMUSG00000041440: 2307 Updated longest transcript for ENSMUSG00000019179: 882 Updated longest transcript for ENSMUSG00000026811: 1552 Updated longest transcript for ENSMUSG00000027935: 802 Updated longest transcript for ENSMUSG00000095976: 1142 Updated longest transcript for ENSMUSG00000033854: 977 Updated longest transcript for ENSMUSG00000028647: 1132 Updated longest transcript for ENSMUSG00000087198: 397 Updated longest transcript for ENSMUSG00000060935: 2891 Updated longest transcript for ENSMUSG00000032737: 748 Updated longest transcript for ENSMUSG00000089943: 573 Updated longest transcript for ENSMUSG00000044408: 1040 Updated longest transcript for ENSMUSG00000052496: 683 Updated longest transcript for ENSMUSG00000106445: 362 Updated longest transcript for ENSMUSG00000018286: 70 Updated longest transcript for ENSMUSG00000022822: 921 Updated longest transcript for ENSMUSG00000030610: 451 Updated longest transcript for ENSMUSG00000118396: 457 Updated longest transcript for ENSMUSG00000051276: 111 Updated longest transcript for ENSMUSG00000049112: 3391 Updated longest transcript for ENSMUSG00000026141: 28 Updated longest transcript for ENSMUSG00000066804: 1501 Updated longest transcript for ENSMUSG00000062687: 1812 Updated longest transcript for ENSMUSG00000093950: 141 Updated longest transcript for ENSMUSG00000036281: 276 Updated 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longest transcript for ENSMUSG00000078258: 21 Updated longest transcript for ENSMUSG00000038738: 3235 Updated longest transcript for ENSMUSG00000020019: 1354 Updated longest transcript for ENSMUSG00000020583: 1453 Updated longest transcript for ENSMUSG00000030711: 539 Updated longest transcript for ENSMUSG00000063193: 2112 Updated longest transcript for ENSMUSG00000071415: 555 Updated longest transcript for ENSMUSG00000024076: 485 Updated longest transcript for ENSMUSG00000070379: 2058 Updated longest transcript for ENSMUSG00000062121: 1314 Updated longest transcript for ENSMUSG00000078680: 273 Updated longest transcript for ENSMUSG00000045306: 3386 Updated longest transcript for ENSMUSG00000073830: 288 Updated longest transcript for ENSMUSG00000099913: 1078 Updated longest transcript for ENSMUSG00000026575: 582 Updated longest transcript for ENSMUSG00000061702: 201 Updated longest transcript for ENSMUSG00000052955: 135 Updated longest transcript for ENSMUSG00000038351: 1546 Updated longest transcript for ENSMUSG00000057409: 808 Updated longest transcript for ENSMUSG00000023033: 5250 Updated longest transcript for ENSMUSG00000021720: 1858 Updated longest transcript for ENSMUSG00000025075: 1187 Updated longest transcript for ENSMUSG00000075140: 1908 Updated longest transcript for ENSMUSG00000023982: 99 Updated longest transcript for ENSMUSG00000099908: 2691 Updated longest transcript for ENSMUSG00000078619: 887 Updated longest transcript for ENSMUSG00000022129: 241 Updated longest transcript for ENSMUSG00000025739: 374 Updated longest transcript for ENSMUSG00000038525: 1933 Updated longest transcript for ENSMUSG00000063529: 426 Updated longest transcript for ENSMUSG00000037172: 765 Updated longest transcript for ENSMUSG00000030158: 408 Updated longest transcript for ENSMUSG00000060475: 2607 Updated longest transcript for ENSMUSG00000048875: 389 Updated longest transcript for ENSMUSG00000041794: 2037 Updated longest transcript for ENSMUSG00000000282: 2405 Updated longest transcript for ENSMUSG00000019528: 607 Updated longest transcript for ENSMUSG00000079641: 181 Updated longest transcript for ENSMUSG00000039474: 769 Updated longest transcript for ENSMUSG00000025207: 1400 Updated longest transcript for ENSMUSG00000027637: 479 Updated longest transcript for ENSMUSG00000063455: 5147 Updated longest transcript for ENSMUSG00000031167: 2252 Updated longest transcript for ENSMUSG00000020701: 1014 Updated longest transcript for ENSMUSG00000010517: 2078 Updated longest transcript for ENSMUSG00000045699: 2 Updated longest transcript for ENSMUSG00000009580: 33 Updated longest transcript for ENSMUSG00000091721: 720 Updated longest transcript for ENSMUSG00000002608: 1887 Updated longest transcript for ENSMUSG00000003484: 78 Updated longest transcript for ENSMUSG00000008658: 1713 Updated longest transcript for ENSMUSG00000030707: 108 Updated longest transcript for ENSMUSG00000006095: 102 Updated longest transcript for ENSMUSG00000018377: 2904 Updated 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Updated longest transcript for ENSMUSG00000039917: 3982 Updated longest transcript for ENSMUSG00000091811: 885 Updated longest transcript for ENSMUSG00000062082: 719 Updated longest transcript for ENSMUSG00000044176: 7083 Updated longest transcript for ENSMUSG00000030159: 198 Updated longest transcript for ENSMUSG00000025474: 2016 Updated longest transcript for ENSMUSG00000018848: 101 Updated longest transcript for ENSMUSG00000027160: 1686 Updated longest transcript for ENSMUSG00000061371: 1915 Updated longest transcript for ENSMUSG00000031202: 2535 Updated longest transcript for ENSMUSG00000015363: 2156 Updated longest transcript for ENSMUSG00000051879: 548 Updated longest transcript for ENSMUSG00000015242: 206 Updated longest transcript for ENSMUSG00000038764: 3444 Updated longest transcript for ENSMUSG00000036377: 2877 Updated longest transcript for ENSMUSG00000037022: 4330 Updated longest transcript for ENSMUSG00000008206: 7900 Updated longest transcript for ENSMUSG00000046805: 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Updated longest transcript for ENSMUSG00000002221: 2408 Updated longest transcript for ENSMUSG00000035232: 618 Updated longest transcript for ENSMUSG00000027313: 791 Updated longest transcript for ENSMUSG00000078923: 1463 Updated longest transcript for ENSMUSG00000054843: 2129 Updated longest transcript for ENSMUSG00000029451: 442 Updated longest transcript for ENSMUSG00000021279: 103 Updated longest transcript for ENSMUSG00000053199: 3457 Updated longest transcript for ENSMUSG00000060685: 173 Updated longest transcript for ENSMUSG00000050029: 2873 Updated longest transcript for ENSMUSG00000031380: 520 Updated longest transcript for ENSMUSG00000095653: 192 Updated longest transcript for ENSMUSG00000028539: 431 Updated longest transcript for ENSMUSG00000022899: 1037 Updated longest transcript for ENSMUSG00000045248: 926 Updated longest transcript for ENSMUSG00000020461: 136 Updated longest transcript for ENSMUSG00000078429: 3489 Updated longest transcript for ENSMUSG00000116152: 588 Updated longest transcript for ENSMUSG00000039198: 417 Updated longest transcript for ENSMUSG00000030685: 510 Updated longest transcript for ENSMUSG00000028383: 941 Updated longest transcript for ENSMUSG00000053093: 109 Updated longest transcript for ENSMUSG00000028145: 960 Updated longest transcript for ENSMUSG00000027574: 248 Updated longest transcript for ENSMUSG00000066324: 5220 Updated longest transcript for ENSMUSG00000021281: 1456 Updated longest transcript for ENSMUSG00000022685: 1780 Updated longest transcript for ENSMUSG00000047237: 911 Updated longest transcript for ENSMUSG00000019920: 3123 Updated longest transcript for ENSMUSG00000064065: 4679 Updated longest transcript for ENSMUSG00000062168: 410 Updated longest transcript for ENSMUSG00000051596: 1315 Updated longest transcript for ENSMUSG00000023903: 3475 Updated longest transcript for ENSMUSG00000060630: 1362 Updated longest transcript for ENSMUSG00000040616: 694 Updated longest transcript for ENSMUSG00000035824: 1883 Updated longest transcript for ENSMUSG00000022997: 933 Updated longest transcript for ENSMUSG00000059383: 917 Updated longest transcript for ENSMUSG00000096537: 518 Updated longest transcript for ENSMUSG00000032598: 1099 Updated longest transcript for ENSMUSG00000074037: 2287 Updated longest transcript for ENSMUSG00000042510: 759 Updated longest transcript for ENSMUSG00000052430: 3479 Updated longest transcript for ENSMUSG00000021872: 854 Updated longest transcript for ENSMUSG00000022663: 797 Updated longest transcript for ENSMUSG00000038762: 604 Updated longest transcript for ENSMUSG00000116925: 282 Updated longest transcript for ENSMUSG00000048222: 1887 Updated longest transcript for ENSMUSG00000059208: 496 Updated longest transcript for ENSMUSG00000035376: 2780 Updated longest transcript for ENSMUSG00000058183: 303 Updated longest transcript for ENSMUSG00000057716: 2665 Updated longest transcript for ENSMUSG00000032561: 1458 Updated longest transcript for ENSMUSG00000027229: 1441 Updated longest transcript for ENSMUSG00000025262: 2112 Updated longest transcript for ENSMUSG00000004069: 1105 Updated longest transcript for ENSMUSG00000050328: 92 Updated longest transcript for ENSMUSG00000036435: 524 Updated longest transcript for ENSMUSG00000059183: 849 Updated longest transcript for ENSMUSG00000075028: 2888 Updated longest transcript for ENSMUSG00000041084: 548 Updated longest transcript for ENSMUSG00000027860: 4654 Updated longest transcript for ENSMUSG00000024982: 2586 Updated longest transcript for ENSMUSG00000072674: 495 Updated longest transcript for ENSMUSG00000000326: 951 Updated longest transcript for ENSMUSG00000024972: 1364 Updated longest transcript for ENSMUSG00000109372: 55 Updated longest transcript for ENSMUSG00000096794: 3332 Updated longest transcript for ENSMUSG00000079334: 198 Updated longest transcript for ENSMUSG00000026641: 1021 Updated longest transcript for ENSMUSG00000030162: 2422 Updated longest transcript for ENSMUSG00000007850: 752 Updated longest transcript for ENSMUSG00000019810: 4188 Updated longest transcript for ENSMUSG00000038102: 976 Updated longest transcript for ENSMUSG00000019982: 1238 Updated longest transcript for ENSMUSG00000074997: 380 Updated longest transcript for ENSMUSG00000064262: 2035 Updated longest transcript for ENSMUSG00000040680: 564 Updated longest transcript for ENSMUSG00000031633: 666 Updated longest transcript for ENSMUSG00000042045: 992 Updated longest transcript for ENSMUSG00000058659: 1269 Updated longest transcript for ENSMUSG00000032245: 1119 Updated longest transcript for ENSMUSG00000036241: 2556 Updated longest transcript for ENSMUSG00000101146: 195 Updated longest transcript for ENSMUSG00000021714: 651 Updated longest transcript for ENSMUSG00000103567: 1744 Updated longest transcript for ENSMUSG00000026109: 1789 Updated longest transcript for ENSMUSG00000026918: 2886 Updated longest transcript for ENSMUSG00000054387: 5436 Updated longest transcript for ENSMUSG00000062905: 702 Updated longest transcript for ENSMUSG00000057801: 1730 Updated longest transcript for ENSMUSG00000061615: 73 Updated longest transcript for ENSMUSG00000070000: 1950 Updated longest transcript for ENSMUSG00000020349: 5804 Updated longest transcript for ENSMUSG00000037924: 631 Updated longest transcript for ENSMUSG00000048833: 3637 Updated longest transcript for ENSMUSG00000003435: 3420 Updated longest transcript for ENSMUSG00000046352: 1469 Updated longest transcript for ENSMUSG00000029500: 1157 Updated longest transcript for ENSMUSG00000082976: 47 Updated longest transcript for ENSMUSG00000065968: 687 Updated longest transcript for ENSMUSG00000030880: 717 Updated longest transcript for ENSMUSG00000046697: 602 Updated longest transcript for ENSMUSG00000095212: 1207 Updated longest transcript for ENSMUSG00000025785: 126 Updated longest transcript for ENSMUSG00000027322: 171 Updated longest transcript for ENSMUSG00000025268: 293 Updated longest transcript for ENSMUSG00000017165: 97 Updated longest transcript for ENSMUSG00000057842: 1943 Updated longest transcript for ENSMUSG00000097200: 138 Updated longest transcript for ENSMUSG00000031536: 2957 Updated longest transcript for ENSMUSG00000032577: 1256 Updated longest transcript for ENSMUSG00000013155: 753 Updated longest transcript for ENSMUSG00000027518: 157 Updated longest transcript for ENSMUSG00000003970: 35 Updated longest transcript for ENSMUSG00000049184: 806 Updated longest transcript for ENSMUSG00000040818: 1024 Updated longest transcript for ENSMUSG00000033883: 4122 Updated longest transcript for ENSMUSG00000036333: 1856 Updated longest transcript for ENSMUSG00000027215: 754 Updated longest transcript for ENSMUSG00000066800: 1931 Updated longest transcript for ENSMUSG00000069171: 581 Updated longest transcript for ENSMUSG00000025630: 545 Updated longest transcript for ENSMUSG00000038453: 3174 Updated longest transcript for ENSMUSG00000053977: 2306 Updated longest transcript for ENSMUSG00000106728: 43 Updated longest transcript for ENSMUSG00000057778: 1507 Updated longest transcript for ENSMUSG00000018378: 1783 Updated longest transcript for ENSMUSG00000091556: 267 Updated longest transcript for ENSMUSG00000004085: 5136 Updated longest transcript for ENSMUSG00000041298: 4554 Updated longest transcript for ENSMUSG00000022362: 367 Updated longest transcript for ENSMUSG00000045176: 698 Updated longest transcript for ENSMUSG00000021469: 1285 Updated longest transcript for ENSMUSG00000034135: 2139 Updated longest transcript for ENSMUSG00000034616: 1811 Updated longest transcript for ENSMUSG00000039031: 1552 Updated longest transcript for ENSMUSG00000029343: 85 Updated longest transcript for ENSMUSG00000052539: 2851 Updated longest transcript for ENSMUSG00000074862: 405 Updated longest transcript for ENSMUSG00000046245: 1520 Updated longest transcript for ENSMUSG00000033658: 1591 Updated longest transcript for ENSMUSG00000053873: 167 Updated longest transcript for ENSMUSG00000034303: 847 Updated longest transcript for ENSMUSG00000021686: 559 Updated longest transcript for ENSMUSG00000021676: 833 Updated longest transcript for ENSMUSG00000055681: 431 Updated longest transcript for ENSMUSG00000070690: 607 Updated longest transcript for ENSMUSG00000062556: 121 Updated longest transcript for ENSMUSG00000117789: 2238 Updated longest transcript for ENSMUSG00000041779: 5263 Updated longest transcript for ENSMUSG00000048299: 1426 Updated longest transcript for ENSMUSG00000030000: 5628 Updated longest transcript for ENSMUSG00000053856: 961 Updated longest transcript for ENSMUSG00000039176: 3730 Updated longest transcript for ENSMUSG00000069430: 1250 Updated longest transcript for ENSMUSG00000020183: 3832 Updated longest transcript for ENSMUSG00000022034: 928 Updated longest transcript for ENSMUSG00000028945: 841 Updated longest transcript for ENSMUSG00000080809: 3702 Updated longest transcript for ENSMUSG00000054874: 418 Updated longest transcript for ENSMUSG00000056973: 187 Updated longest transcript for ENSMUSG00000014444: 3305 Updated longest transcript for ENSMUSG00000020272: 2026 Updated longest transcript for ENSMUSG00000063851: 445 Updated longest transcript for ENSMUSG00000025134: 2732 Updated longest transcript for ENSMUSG00000020576: 145 Updated longest transcript for ENSMUSG00000022681: 137 Updated longest transcript for ENSMUSG00000026650: 152 Updated longest transcript for ENSMUSG00000022508: 1073 Updated longest transcript for ENSMUSG00000030595: 763 Updated longest transcript for ENSMUSG00000035107: 3938 Updated longest transcript for ENSMUSG00000090512: 959 Updated longest transcript for ENSMUSG00000018040: 3453 Updated longest transcript for ENSMUSG00000032609: 631 Updated longest transcript for ENSMUSG00000042104: 1852 Updated longest transcript for ENSMUSG00000035798: 3058 Updated longest transcript for ENSMUSG00000027669: 1122 Updated longest transcript for ENSMUSG00000026224: 229 Updated longest transcript for ENSMUSG00000009927: 286 Updated longest transcript for ENSMUSG00000032268: 1619 Updated longest transcript for ENSMUSG00000079342: 1036 Updated longest transcript for ENSMUSG00000045594: 1770 Updated longest transcript for ENSMUSG00000117621: 166 Updated longest transcript for ENSMUSG00000020610: 1433 Updated longest transcript for ENSMUSG00000031916: 413 Updated longest transcript for ENSMUSG00000042189: 122 Updated longest transcript for ENSMUSG00000051234: 797 Updated longest transcript for ENSMUSG00000024973: 122 Updated longest transcript for ENSMUSG00000041268: 307 Updated longest transcript for ENSMUSG00000041872: 621 Updated longest transcript for ENSMUSG00000034796: 605 Updated longest transcript for ENSMUSG00000023118: 3976 Updated longest transcript for ENSMUSG00000078922: 1302 Updated longest transcript for ENSMUSG00000044121: 221 Updated longest transcript for ENSMUSG00000017188: 2 Updated longest transcript for ENSMUSG00000048810: 56 Updated longest transcript for ENSMUSG00000024084: 610 Updated longest transcript for ENSMUSG00000037820: 1399 Updated longest transcript for ENSMUSG00000021953: 381 Updated longest transcript for ENSMUSG00000079594: 57 Updated longest transcript for ENSMUSG00000030889: 860 Updated longest transcript for ENSMUSG00000037847: 1185 Updated longest transcript for ENSMUSG00000031165: 481 Updated longest transcript for ENSMUSG00000026616: 3025 Updated longest transcript for ENSMUSG00000115904: 2176 Updated longest transcript for ENSMUSG00000026669: 1679 Updated longest transcript for ENSMUSG00000026203: 1875 Updated longest transcript for ENSMUSG00000027508: 6288 Updated longest transcript for ENSMUSG00000055850: 1006 Updated longest transcript for ENSMUSG00000020914: 511 Updated longest transcript for ENSMUSG00000027111: 927 Updated longest transcript for ENSMUSG00000095710: 553 Updated longest transcript for ENSMUSG00000059554: 1507 Updated longest transcript for ENSMUSG00000032030: 3234 Updated longest transcript for ENSMUSG00000022946: 243 Updated longest transcript for ENSMUSG00000022893: 1574 Updated longest transcript for ENSMUSG00000049598: 413 Updated longest transcript for ENSMUSG00000062017: 174 Updated longest transcript for ENSMUSG00000030094: 2254 Updated longest transcript for ENSMUSG00000067543: 101 Updated longest transcript for ENSMUSG00000068399: 378 Updated longest transcript for ENSMUSG00000028189: 1759 Updated longest transcript for ENSMUSG00000039477: 1320 Updated longest transcript for ENSMUSG00000004591: 2909 Updated longest transcript for ENSMUSG00000039062: 421 Updated longest transcript for ENSMUSG00000022780: 1799 Updated longest transcript for ENSMUSG00000096706: 119 Updated longest transcript for ENSMUSG00000073768: 128 Updated longest transcript for ENSMUSG00000005503: 135 Updated longest transcript for ENSMUSG00000096898: 134 Updated longest transcript for ENSMUSG00000029320: 782 Updated longest transcript for ENSMUSG00000055960: 2318 Updated longest transcript for ENSMUSG00000024038: 355 Updated longest transcript for ENSMUSG00000014850: 3870 Updated longest transcript for ENSMUSG00000030786: 1114 Updated longest transcript for ENSMUSG00000032489: 680 Updated longest transcript for ENSMUSG00000044430: 191 Updated longest transcript for ENSMUSG00000030681: 123 Updated longest transcript for ENSMUSG00000020171: 870 Updated longest transcript for ENSMUSG00000026834: 3096 Updated longest transcript for ENSMUSG00000068250: 611 Updated longest transcript for ENSMUSG00000020454: 499 Updated longest transcript for ENSMUSG00000026775: 2229 Updated longest transcript for ENSMUSG00000043881: 2234 Updated longest transcript for ENSMUSG00000024154: 163 Updated longest transcript for ENSMUSG00000046532: 6335 Updated longest transcript for ENSMUSG00000085684: 228 Updated longest transcript for ENSMUSG00000029027: 1089 Updated longest transcript for ENSMUSG00000040694: 424 Updated longest transcript for ENSMUSG00000066607: 93 Updated longest transcript for ENSMUSG00000074679: 115 Updated longest transcript for ENSMUSG00000036172: 690 Updated longest transcript for ENSMUSG00000041712: 1921 Updated longest transcript for ENSMUSG00000039047: 3514 Updated longest transcript for ENSMUSG00000021484: 3044 Updated longest transcript for ENSMUSG00000026341: 1111 Updated longest transcript for ENSMUSG00000049354: 4469 Updated longest transcript for ENSMUSG00000048126: 732 Updated longest transcript for ENSMUSG00000034898: 640 Updated longest transcript for ENSMUSG00000025583: 4862 Updated longest transcript for ENSMUSG00000029860: 1400 Updated longest transcript for ENSMUSG00000027242: 2298 Updated longest transcript for ENSMUSG00000052562: 1081 Updated longest transcript for ENSMUSG00000030965: 1607 Updated longest transcript for ENSMUSG00000048410: 1041 Updated longest transcript for ENSMUSG00000025759: 1082 Updated longest transcript for ENSMUSG00000026514: 1389 Updated longest transcript for ENSMUSG00000070476: 3020 Updated longest transcript for ENSMUSG00000003379: 653 Updated longest transcript for ENSMUSG00000038335: 2058 Updated longest transcript for ENSMUSG00000000131: 863 Updated longest transcript for ENSMUSG00000021466: 9012 Updated longest transcript for ENSMUSG00000022931: 345 Updated longest transcript for ENSMUSG00000032316: 927 Updated longest transcript for ENSMUSG00000059504: 1540 Updated longest transcript for ENSMUSG00000076752: 411 Updated longest transcript for ENSMUSG00000094793: 272 Updated longest transcript for ENSMUSG00000058729: 1321 Updated longest transcript for ENSMUSG00000029594: 1193 Updated longest transcript for ENSMUSG00000010592: 1854 Updated longest transcript for ENSMUSG00000095341: 122 Updated longest transcript for ENSMUSG00000092329: 2264 Updated longest transcript for ENSMUSG00000026885: 1939 Updated longest transcript for ENSMUSG00000045767: 3858 Updated longest transcript for ENSMUSG00000029202: 4476 Updated longest transcript for ENSMUSG00000039246: 798 Updated longest transcript for ENSMUSG00000097064: 2173 Updated longest transcript for ENSMUSG00000039323: 312 Updated longest transcript for ENSMUSG00000029223: 306 Updated longest transcript for ENSMUSG00000023965: 2819 Updated longest transcript for ENSMUSG00000038388: 268 Updated longest transcript for ENSMUSG00000050675: 422 Updated longest transcript for ENSMUSG00000094157: 757 Updated longest transcript for ENSMUSG00000017310: 221 Updated longest transcript for ENSMUSG00000004460: 1260 Updated longest transcript for ENSMUSG00000005540: 1073 Updated longest transcript for ENSMUSG00000081650: 56 Updated longest transcript for ENSMUSG00000029486: 2372 Updated longest transcript for ENSMUSG00000019883: 811 Updated longest transcript for ENSMUSG00000047462: 1555 Updated longest transcript for ENSMUSG00000039518: 919 Updated longest transcript for ENSMUSG00000029715: 209 Updated longest transcript for ENSMUSG00000029265: 1861 Updated longest transcript for ENSMUSG00000045482: 820 Updated longest transcript for ENSMUSG00000023914: 629 Updated longest transcript for ENSMUSG00000029073: 1634 Updated longest transcript for ENSMUSG00000069208: 463 Updated longest transcript for ENSMUSG00000034687: 2957 Updated longest transcript for ENSMUSG00000053477: 4895 Updated longest transcript for ENSMUSG00000056025: 2001 Updated longest transcript for ENSMUSG00000026878: 2065 Updated longest transcript for ENSMUSG00000105096: 1606 Updated longest transcript for ENSMUSG00000042514: 2123 Updated longest transcript for ENSMUSG00000092162: 4637 Updated longest transcript for ENSMUSG00000055204: 2116 Updated longest transcript for ENSMUSG00000073069: 463 Updated longest transcript for ENSMUSG00000025962: 2251 Updated longest transcript for ENSMUSG00000106966: 1661 Updated longest transcript for ENSMUSG00000029335: 4534 Updated longest transcript for ENSMUSG00000021760: 478 Updated longest transcript for ENSMUSG00000096154: 583 Updated longest transcript for ENSMUSG00000020598: 2173 Updated longest transcript for ENSMUSG00000032666: 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Updated longest transcript for ENSMUSG00000047171: 197 Updated longest transcript for ENSMUSG00000081683: 1127 Updated longest transcript for ENSMUSG00000045917: 2388 Updated longest transcript for ENSMUSG00000060166: 2453 Updated longest transcript for ENSMUSG00000026158: 3325 Updated longest transcript for ENSMUSG00000061244: 10071 Updated longest transcript for ENSMUSG00000031134: 1990 Updated longest transcript for ENSMUSG00000093833: 762 Updated longest transcript for ENSMUSG00000037172: 5364 Updated longest transcript for ENSMUSG00000038861: 903 Updated longest transcript for ENSMUSG00000005370: 87 Updated longest transcript for ENSMUSG00000027620: 1694 Updated longest transcript for ENSMUSG00000079584: 115 Updated longest transcript for ENSMUSG00000035095: 2858 Updated longest transcript for ENSMUSG00000034484: 472 Updated longest transcript for ENSMUSG00000043190: 1290 Updated longest transcript for ENSMUSG00000047767: 4526 Updated longest transcript for ENSMUSG00000073460: 791 Updated longest transcript for ENSMUSG00000061650: 1430 Updated longest transcript for ENSMUSG00000052707: 3397 Updated longest transcript for ENSMUSG00000037348: 2364 Updated longest transcript for ENSMUSG00000079014: 734 Updated longest transcript for ENSMUSG00000028776: 596 Updated longest transcript for ENSMUSG00000030172: 4821 Updated longest transcript for ENSMUSG00000093927: 137 Updated longest transcript for ENSMUSG00000062604: 4272 Updated longest transcript for ENSMUSG00000039676: 182 Updated longest transcript for ENSMUSG00000050650: 59 Updated longest transcript for ENSMUSG00000037738: 1120 Updated longest transcript for ENSMUSG00000025328: 1036 Updated longest transcript for ENSMUSG00000025747: 332 Updated longest transcript for ENSMUSG00000044664: 544 Updated longest transcript for ENSMUSG00000039501: 932 Updated longest transcript for ENSMUSG00000026771: 5096 Updated longest transcript for ENSMUSG00000039347: 1407 Updated longest transcript for ENSMUSG00000006392: 994 Updated longest transcript for ENSMUSG00000067438: 527 Updated longest transcript for ENSMUSG00000027347: 2549 Updated longest transcript for ENSMUSG00000022738: 510 Updated longest transcript for ENSMUSG00000032940: 1925 Updated longest transcript for ENSMUSG00000032342: 1390 Updated longest transcript for ENSMUSG00000037406: 640 Updated longest transcript for ENSMUSG00000028977: 2318 Updated longest transcript for ENSMUSG00000028298: 227 Updated longest transcript for ENSMUSG00000028495: 42 Updated longest transcript for ENSMUSG00000029814: 2119 Updated longest transcript for ENSMUSG00000030001: 120 Updated longest transcript for ENSMUSG00000071745: 169 Updated longest transcript for ENSMUSG00000072295: 417 Updated longest transcript for ENSMUSG00000069089: 847 Updated longest transcript for ENSMUSG00000062376: 1816 Updated longest transcript for ENSMUSG00000025420: 1857 Updated longest transcript for ENSMUSG00000042308: 643 Updated longest transcript for ENSMUSG00000034538: 564 Updated longest transcript for ENSMUSG00000022684: 1355 Updated longest transcript for ENSMUSG00000031698: 1158 Updated longest transcript for ENSMUSG00000020029: 2281 Updated longest transcript for ENSMUSG00000027425: 880 Updated longest transcript for ENSMUSG00000032553: 66 Updated longest transcript for ENSMUSG00000074781: 3345 Updated longest transcript for ENSMUSG00000039449: 2388 Updated longest transcript for ENSMUSG00000067684: 213 Updated longest transcript for ENSMUSG00000003234: 864 Updated longest transcript for ENSMUSG00000035437: 1710 Updated longest transcript for ENSMUSG00000042684: 358 Updated longest transcript for ENSMUSG00000024943: 2377 Updated longest transcript for ENSMUSG00000031145: 455 Updated longest transcript for ENSMUSG00000024228: 2563 Updated longest transcript for ENSMUSG00000024534: 2169 Updated longest transcript for ENSMUSG00000108605: 623 Updated longest transcript for ENSMUSG00000069266: 77 Updated longest transcript for ENSMUSG00000001707: 1963 Updated longest transcript for ENSMUSG00000039485: 2586 Updated longest transcript for ENSMUSG00000037991: 3261 Updated longest transcript for ENSMUSG00000019867: 796 Updated longest transcript for ENSMUSG00000037326: 1946 Updated longest transcript for ENSMUSG00000079355: 546 Updated longest transcript for ENSMUSG00000003534: 1734 Updated longest transcript for ENSMUSG00000055531: 4798 Updated longest transcript for ENSMUSG00000026020: 7723 Updated longest transcript for ENSMUSG00000033701: 323 Updated longest transcript for ENSMUSG00000090304: 6508 Updated longest transcript for ENSMUSG00000093806: 4 Updated longest transcript for ENSMUSG00000022314: 1816 Updated longest transcript for ENSMUSG00000037049: 356 Updated longest transcript for ENSMUSG00000036864: 381 Updated longest transcript for ENSMUSG00000048329: 160 Updated longest transcript for ENSMUSG00000026141: 696 Updated longest transcript for ENSMUSG00000102918: 1744 Updated longest transcript for ENSMUSG00000101678: 448 Updated longest transcript for ENSMUSG00000069720: 356 Updated longest transcript for ENSMUSG00000021224: 3046 Updated longest transcript for ENSMUSG00000005148: 1841 Updated longest transcript for ENSMUSG00000027854: 6650 Updated longest transcript for ENSMUSG00000000823: 2719 Updated longest transcript for ENSMUSG00000027752: 100 Updated longest transcript for ENSMUSG00000021750: 2416 Updated longest transcript for ENSMUSG00000050944: 241 Updated longest transcript for ENSMUSG00000034024: 245 Updated longest transcript for ENSMUSG00000042659: 2482 Updated longest transcript for ENSMUSG00000028654: 1970 Updated longest transcript for ENSMUSG00000021715: 436 Updated longest transcript for ENSMUSG00000063900: 102 Updated longest transcript for ENSMUSG00000048978: 1313 Updated longest transcript for ENSMUSG00000090700: 434 Updated longest transcript for ENSMUSG00000039850: 4010 Updated longest transcript for ENSMUSG00000029727: 1337 Updated longest transcript for ENSMUSG00000067786: 888 Updated longest transcript for ENSMUSG00000052146: 177 Updated longest transcript for ENSMUSG00000028979: 978 Updated longest transcript for ENSMUSG00000025357: 1629 Updated longest transcript for ENSMUSG00000028859: 602 Updated longest transcript for ENSMUSG00000031634: 1289 Updated longest transcript for ENSMUSG00000022021: 956 Updated longest transcript for ENSMUSG00000039953: 335 Updated longest transcript for ENSMUSG00000032758: 99 Updated longest transcript for ENSMUSG00000063851: 447 Updated longest transcript for ENSMUSG00000074627: 236 Updated longest transcript for ENSMUSG00000046834: 474 Updated longest transcript for ENSMUSG00000001168: 195 Updated longest transcript for ENSMUSG00000022119: 3031 Updated longest transcript for ENSMUSG00000032377: 1649 Updated longest transcript for ENSMUSG00000098639: 976 Updated longest transcript for ENSMUSG00000094840: 1224 Updated longest transcript for ENSMUSG00000073421: 311 Updated longest transcript for ENSMUSG00000024085: 2538 Updated longest transcript for ENSMUSG00000035407: 1565 Updated longest transcript for ENSMUSG00000043932: 3100 Updated longest transcript for ENSMUSG00000046589: 1306 Updated longest transcript for ENSMUSG00000028024: 1150 Updated longest transcript for ENSMUSG00000117874: 663 Updated longest transcript for ENSMUSG00000029784: 346 Updated longest transcript for ENSMUSG00000035933: 3310 Updated longest transcript for ENSMUSG00000028033: 3760 Updated longest transcript for ENSMUSG00000033257: 6031 Updated longest transcript for ENSMUSG00000038301: 1717 Updated longest transcript for ENSMUSG00000042265: 2276 Updated longest transcript for ENSMUSG00000028558: 570 Updated longest transcript for ENSMUSG00000032292: 616 Updated longest transcript for ENSMUSG00000063632: 6957 Updated longest transcript for ENSMUSG00000033589: 438 Updated longest transcript for ENSMUSG00000039057: 1091 Updated longest transcript for ENSMUSG00000025747: 413 Updated longest transcript for ENSMUSG00000022962: 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Updated longest transcript for ENSMUSG00000051504: 1948 Updated longest transcript for ENSMUSG00000049488: 2911 Updated longest transcript for ENSMUSG00000070806: 300 Updated longest transcript for ENSMUSG00000090369: 281 Updated longest transcript for ENSMUSG00000028797: 1290 Updated longest transcript for ENSMUSG00000074434: 94 Updated longest transcript for ENSMUSG00000073811: 159 Updated longest transcript for ENSMUSG00000022946: 837 Updated longest transcript for ENSMUSG00000021209: 999 Updated longest transcript for ENSMUSG00000024548: 4835 Updated longest transcript for ENSMUSG00000028037: 1458 Updated longest transcript for ENSMUSG00000044807: 3478 Updated longest transcript for ENSMUSG00000072762: 115 Updated longest transcript for ENSMUSG00000032349: 1711 Updated longest transcript for ENSMUSG00000068697: 271 Updated longest transcript for ENSMUSG00000038384: 2623 Updated longest transcript for ENSMUSG00000040253: 3575 Updated longest transcript for ENSMUSG00000094724: 600 Updated longest transcript for ENSMUSG00000026764: 3621 Updated longest transcript for ENSMUSG00000031957: 68 Updated longest transcript for ENSMUSG00000023921: 37 Updated longest transcript for ENSMUSG00000087598: 4135 Updated longest transcript for ENSMUSG00000043131: 3522 Updated longest transcript for ENSMUSG00000024050: 6063 Updated longest transcript for ENSMUSG00000017064: 309 Updated longest transcript for ENSMUSG00000018965: 771 Updated longest transcript for ENSMUSG00000031837: 470 Updated longest transcript for ENSMUSG00000036502: 2186 Updated longest transcript for ENSMUSG00000004462: 1856 Updated longest transcript for ENSMUSG00000022814: 3087 Updated longest transcript for ENSMUSG00000036083: 2603 Updated longest transcript for ENSMUSG00000039182: 1267 Updated longest transcript for ENSMUSG00000023921: 1292 Updated longest transcript for ENSMUSG00000063846: 155 Updated longest transcript for ENSMUSG00000031803: 1848 Updated longest transcript for ENSMUSG00000032180: 694 Updated longest transcript for ENSMUSG00000038146: 1000 Updated longest transcript for ENSMUSG00000043445: 1543 Updated longest transcript for ENSMUSG00000066829: 1477 Updated longest transcript for ENSMUSG00000057817: 1383 Updated longest transcript for ENSMUSG00000030292: 61 Updated longest transcript for ENSMUSG00000032766: 285 Updated longest transcript for ENSMUSG00000021000: 2561 Updated longest transcript for ENSMUSG00000024622: 2873 Updated longest transcript for ENSMUSG00000028062: 129 Updated longest transcript for ENSMUSG00000044365: 4064 Updated longest transcript for ENSMUSG00000041840: 136 Updated longest transcript for ENSMUSG00000098112: 458 Updated longest transcript for ENSMUSG00000028115: 554 Updated longest transcript for ENSMUSG00000110868: 422 Updated longest transcript for ENSMUSG00000090362: 9251 Updated longest transcript for ENSMUSG00000029038: 305 Updated longest transcript for ENSMUSG00000030435: 1169 Updated longest transcript for ENSMUSG00000091586: 1249 Updated longest transcript for ENSMUSG00000041319: 188 Updated longest transcript for ENSMUSG00000021460: 2718 Updated longest transcript for ENSMUSG00000062608: 45 Updated longest transcript for ENSMUSG00000020900: 1561 Updated longest transcript for ENSMUSG00000047528: 304 Updated longest transcript for ENSMUSG00000036594: 300 Updated longest transcript for ENSMUSG00000045954: 1538 Updated longest transcript for ENSMUSG00000022983: 347 Updated longest transcript for ENSMUSG00000024799: 944 Updated longest transcript for ENSMUSG00000009090: 999 Updated longest transcript for ENSMUSG00000031285: 7709 Updated longest transcript for ENSMUSG00000045404: 797 Updated longest transcript for ENSMUSG00000029203: 4039 Updated longest transcript for ENSMUSG00000094910: 5266 Updated longest transcript for ENSMUSG00000033021: 323 Updated longest transcript for ENSMUSG00000006289: 2265 Updated longest transcript for ENSMUSG00000026882: 161 Updated longest transcript for ENSMUSG00000023828: 2943 Updated longest transcript for ENSMUSG00000038914: 2491 Updated longest transcript for ENSMUSG00000014232: 773 Updated longest transcript for ENSMUSG00000042831: 122 Updated longest transcript for ENSMUSG00000027531: 3230 Updated longest transcript for ENSMUSG00000073490: 305 Updated longest transcript for ENSMUSG00000024376: 1015 Updated longest transcript for ENSMUSG00000079261: 75 Updated longest transcript for ENSMUSG00000045216: 2225 Updated longest transcript for ENSMUSG00000076430: 271 Updated longest transcript for ENSMUSG00000085079: 1474 Updated longest transcript for ENSMUSG00000020709: 528 Updated longest transcript for ENSMUSG00000031452: 648 Updated longest transcript for ENSMUSG00000037669: 1855 Updated longest transcript for ENSMUSG00000089840: 1050 Updated longest transcript for ENSMUSG00000020608: 4807 Updated longest transcript for ENSMUSG00000053719: 48 Updated longest transcript for ENSMUSG00000026674: 5547 Updated longest transcript for ENSMUSG00000047669: 584 Updated longest transcript for ENSMUSG00000068940: 618 Updated longest transcript for ENSMUSG00000035681: 3953 Updated longest transcript for ENSMUSG00000074151: 1197 Updated longest transcript for ENSMUSG00000092473: 2360 Updated longest transcript for ENSMUSG00000025900: 454 Updated longest transcript for ENSMUSG00000035900: 3828 Updated longest transcript for ENSMUSG00000097708: 2040 Updated longest transcript for ENSMUSG00000026848: 2440 Updated longest transcript for ENSMUSG00000041605: 795 Updated longest transcript for ENSMUSG00000035545: 2589 Updated longest transcript for ENSMUSG00000053825: 4863 Updated longest transcript for ENSMUSG00000045128: 377 Updated longest transcript for ENSMUSG00000055862: 135 Updated longest transcript for ENSMUSG00000020437: 128 Updated longest transcript for ENSMUSG00000031119: 500 Updated longest transcript for ENSMUSG00000022122: 2441 Updated longest transcript for ENSMUSG00000033106: 305 Updated longest transcript for ENSMUSG00000023094: 621 Updated longest transcript for ENSMUSG00000064264: 150 Updated longest transcript for ENSMUSG00000040146: 2295 Updated longest transcript for ENSMUSG00000036469: 3107 Updated longest transcript for ENSMUSG00000095621: 602 Updated longest transcript for ENSMUSG00000038400: 3364 Updated longest transcript for ENSMUSG00000034216: 834 Updated longest transcript for ENSMUSG00000064247: 2875 Updated longest transcript for ENSMUSG00000067656: 389 Updated longest transcript for ENSMUSG00000047539: 3789 Updated longest transcript for ENSMUSG00000028990: 530 Updated longest transcript for ENSMUSG00000048583: 3050 Updated longest transcript for ENSMUSG00000037014: 139 Updated longest transcript for ENSMUSG00000052212: 235 Updated longest transcript for ENSMUSG00000034201: 1287 Updated longest transcript for ENSMUSG00000002297: 859 Updated longest transcript for ENSMUSG00000073894: 249 Updated longest transcript for ENSMUSG00000006642: 3369 Updated longest transcript for ENSMUSG00000050335: 408 Updated longest transcript for ENSMUSG00000070337: 2337 Updated longest transcript for ENSMUSG00000032908: 2863 Updated longest transcript for ENSMUSG00000021950: 821 Updated longest transcript for ENSMUSG00000034744: 885 Updated longest transcript for ENSMUSG00000028885: 428 Updated longest transcript for ENSMUSG00000028745: 766 Updated longest transcript for ENSMUSG00000025317: 238 Updated longest transcript for ENSMUSG00000028583: 74 Updated longest transcript for ENSMUSG00000090093: 443 Updated longest transcript for ENSMUSG00000049908: 5456 Updated longest transcript for ENSMUSG00000048516: 80 Updated longest transcript for ENSMUSG00000024223: 857 Updated longest transcript for ENSMUSG00000090264: 298 Updated longest transcript for ENSMUSG00000058248: 4868 Updated longest transcript for ENSMUSG00000028545: 391 Updated longest transcript for ENSMUSG00000096901: 306 Updated longest transcript for ENSMUSG00000037242: 3132 Updated longest transcript for ENSMUSG00000068574: 97 Updated 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Updated longest transcript for ENSMUSG00000089739: 1251 Updated longest transcript for ENSMUSG00000032182: 965 Updated longest transcript for ENSMUSG00000110641: 608 Updated longest transcript for ENSMUSG00000029699: 669 Updated longest transcript for ENSMUSG00000026459: 753 Updated longest transcript for ENSMUSG00000029063: 1526 Updated longest transcript for ENSMUSG00000031896: 52 Updated longest transcript for ENSMUSG00000108114: 1331 Updated longest transcript for ENSMUSG00000045555: 363 Updated longest transcript for ENSMUSG00000020671: 2359 Updated longest transcript for ENSMUSG00000090487: 949 Updated longest transcript for ENSMUSG00000003929: 4901 Updated longest transcript for ENSMUSG00000026940: 40 Updated longest transcript for ENSMUSG00000025431: 626 Updated longest transcript for ENSMUSG00000096351: 758 Updated longest transcript for ENSMUSG00000078580: 1755 Updated longest transcript for ENSMUSG00000003746: 1209 Updated longest transcript for ENSMUSG00000045374: 825 Updated longest transcript for ENSMUSG00000035683: 788 Updated longest transcript for ENSMUSG00000100486: 307 Updated longest transcript for ENSMUSG00000024729: 121 Updated longest transcript for ENSMUSG00000095026: 541 Updated longest transcript for ENSMUSG00000079808: 392 Updated longest transcript for ENSMUSG00000036057: 171 Updated longest transcript for ENSMUSG00000032081: 290 Updated longest transcript for ENSMUSG00000067369: 2569 Updated longest transcript for ENSMUSG00000034940: 26 Updated longest transcript for ENSMUSG00000002778: 747 Updated longest transcript for ENSMUSG00000048806: 201 Updated longest transcript for ENSMUSG00000036390: 563 Updated longest transcript for ENSMUSG00000036955: 1603 Updated longest transcript for ENSMUSG00000109523: 110 Updated longest transcript for ENSMUSG00000096365: 1216 Updated longest transcript for ENSMUSG00000046679: 1049 Updated longest transcript for ENSMUSG00000060180: 84 Updated longest transcript for ENSMUSG00000030158: 680 Updated 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Updated longest transcript for ENSMUSG00000102697: 2401 Updated longest transcript for ENSMUSG00000038522: 1047 Updated longest transcript for ENSMUSG00000034220: 2215 Updated longest transcript for ENSMUSG00000057842: 2064 Updated longest transcript for ENSMUSG00000090799: 2807 Updated longest transcript for ENSMUSG00000017119: 1435 Updated longest transcript for ENSMUSG00000041044: 2355 Updated longest transcript for ENSMUSG00000037408: 2158 Updated longest transcript for ENSMUSG00000067604: 402 Updated longest transcript for ENSMUSG00000025929: 637 Updated longest transcript for ENSMUSG00000061701: 52 Updated longest transcript for ENSMUSG00000020437: 150 Updated longest transcript for ENSMUSG00000090356: 278 Updated longest transcript for ENSMUSG00000030966: 1201 Updated longest transcript for ENSMUSG00000002944: 1660 Updated longest transcript for ENSMUSG00000091625: 237 Updated longest transcript for ENSMUSG00000032667: 1174 Updated longest transcript for ENSMUSG00000039540: 51 Updated longest transcript for ENSMUSG00000071766: 143 Updated longest transcript for ENSMUSG00000049229: 632 Updated longest transcript for ENSMUSG00000091652: 326 Updated longest transcript for ENSMUSG00000019948: 460 Updated longest transcript for ENSMUSG00000031085: 1286 Updated longest transcript for ENSMUSG00000057897: 2184 Updated longest transcript for ENSMUSG00000044217: 568 Updated longest transcript for ENSMUSG00000069135: 2209 Updated longest transcript for ENSMUSG00000045053: 1777 Updated longest transcript for ENSMUSG00000024283: 2889 Updated longest transcript for ENSMUSG00000057363: 3265 Updated longest transcript for ENSMUSG00000063663: 6000 Updated longest transcript for ENSMUSG00000053646: 1973 Updated longest transcript for ENSMUSG00000043286: 2228 Updated longest transcript for ENSMUSG00000072623: 2385 Updated longest transcript for ENSMUSG00000110266: 3447 Updated longest transcript for ENSMUSG00000056910: 9517 Updated longest transcript for ENSMUSG00000043719: 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longest transcript for ENSMUSG00000031696: 738 Updated longest transcript for ENSMUSG00000073633: 302 Updated longest transcript for ENSMUSG00000072259: 314 Updated longest transcript for ENSMUSG00000023966: 162 Updated longest transcript for ENSMUSG00000035547: 2314 Updated longest transcript for ENSMUSG00000061032: 408 Updated longest transcript for ENSMUSG00000026393: 4125 Updated longest transcript for ENSMUSG00000078768: 257 Updated longest transcript for ENSMUSG00000019970: 1076 Updated longest transcript for ENSMUSG00000026466: 2326 Updated longest transcript for ENSMUSG00000075120: 3080 Updated longest transcript for ENSMUSG00000033777: 859 Updated longest transcript for ENSMUSG00000040690: 1841 Updated longest transcript for ENSMUSG00000025375: 1091 Updated longest transcript for ENSMUSG00000038637: 608 Updated longest transcript for ENSMUSG00000068606: 1461 Updated longest transcript for ENSMUSG00000020890: 4918 Updated longest transcript for ENSMUSG00000031754: 4187 Updated longest transcript for ENSMUSG00000094838: 138 Updated longest transcript for ENSMUSG00000038866: 2799 Updated longest transcript for ENSMUSG00000031196: 140 Updated longest transcript for ENSMUSG00000076436: 149 Updated longest transcript for ENSMUSG00000028854: 1375 Updated longest transcript for ENSMUSG00000042812: 1265 Updated longest transcript for ENSMUSG00000044337: 1842 Updated longest transcript for ENSMUSG00000031179: 426 Updated longest transcript for ENSMUSG00000042377: 2224 Updated longest transcript for ENSMUSG00000092356: 383 Updated longest transcript for ENSMUSG00000064220: 109 Updated longest transcript for ENSMUSG00000038463: 464 Updated longest transcript for ENSMUSG00000032594: 2810 Updated longest transcript for ENSMUSG00000020898: 3725 Updated longest transcript for ENSMUSG00000020358: 1308 Updated longest transcript for ENSMUSG00000019851: 1197 Updated longest transcript for ENSMUSG00000022652: 88 Updated longest transcript for ENSMUSG00000057068: 786 Updated longest transcript for ENSMUSG00000098571: 1783 Updated longest transcript for ENSMUSG00000009580: 515 Updated longest transcript for ENSMUSG00000059873: 2694 Updated longest transcript for ENSMUSG00000032758: 202 Updated longest transcript for ENSMUSG00000020986: 1614 Updated longest transcript for ENSMUSG00000025044: 2518 Updated longest transcript for ENSMUSG00000075610: 1430 Updated longest transcript for ENSMUSG00000051319: 608 Updated longest transcript for ENSMUSG00000044103: 772 Updated longest transcript for ENSMUSG00000046402: 226 Updated longest transcript for ENSMUSG00000070552: 58 Updated longest transcript for ENSMUSG00000005267: 1199 Updated longest transcript for ENSMUSG00000035394: 1893 Updated longest transcript for ENSMUSG00000032485: 149 Updated longest transcript for ENSMUSG00000075079: 1038 Updated longest transcript for ENSMUSG00000054934: 377 Updated longest transcript for ENSMUSG00000062328: 140 Updated longest transcript for ENSMUSG00000023015: 893 Updated longest transcript for ENSMUSG00000043972: 686 Updated longest transcript for ENSMUSG00000038497: 2050 Updated longest transcript for ENSMUSG00000019737: 315 Updated longest transcript for ENSMUSG00000039057: 1092 Updated longest transcript for ENSMUSG00000032553: 2176 Updated longest transcript for ENSMUSG00000010936: 3830 Updated longest transcript for ENSMUSG00000022698: 3726 Updated longest transcript for ENSMUSG00000031986: 2698 Updated longest transcript for ENSMUSG00000032311: 1849 Updated longest transcript for ENSMUSG00000024990: 243 Updated longest transcript for ENSMUSG00000022849: 3849 Updated longest transcript for ENSMUSG00000063206: 93 Updated longest transcript for ENSMUSG00000056481: 259 Updated longest transcript for ENSMUSG00000059248: 1770 Updated longest transcript for ENSMUSG00000068099: 953 Updated longest transcript for ENSMUSG00000024770: 809 Updated longest transcript for ENSMUSG00000078887: 1035 Updated longest transcript for ENSMUSG00000045281: 858 Updated longest transcript for ENSMUSG00000054136: 420 Updated longest transcript for ENSMUSG00000032050: 2250 Updated longest transcript for ENSMUSG00000103371: 196 Updated longest transcript for ENSMUSG00000035187: 1278 Updated longest transcript for ENSMUSG00000029452: 966 Updated longest transcript for ENSMUSG00000048661: 1980 Updated longest transcript for ENSMUSG00000021377: 1286 Updated longest transcript for ENSMUSG00000028463: 619 Updated longest transcript for ENSMUSG00000021636: 1332 Updated longest transcript for ENSMUSG00000032773: 4272 Updated longest transcript for ENSMUSG00000096795: 508 Updated longest transcript for ENSMUSG00000078867: 1072 Updated longest transcript for ENSMUSG00000027806: 6515 Updated longest transcript for ENSMUSG00000000957: 1328 Updated longest transcript for ENSMUSG00000022041: 1872 Updated longest transcript for ENSMUSG00000031891: 569 Updated longest transcript for ENSMUSG00000025196: 186 Updated longest transcript for ENSMUSG00000075139: 3758 Updated longest transcript for ENSMUSG00000038740: 3492 Updated longest transcript for ENSMUSG00000021830: 3108 Updated longest transcript for ENSMUSG00000116473: 3642 Updated longest transcript for ENSMUSG00000048826: 611 Updated longest transcript for ENSMUSG00000047193: 2179 Updated longest transcript for ENSMUSG00000045438: 2102 Updated longest transcript for ENSMUSG00000032624: 2194 Updated longest transcript for ENSMUSG00000026922: 530 Updated longest transcript for ENSMUSG00000050089: 190 Updated longest transcript for ENSMUSG00000063823: 3132 Updated longest transcript for ENSMUSG00000022456: 3701 Updated longest transcript for ENSMUSG00000044303: 264 Updated longest transcript for ENSMUSG00000057388: 329 Updated longest transcript for ENSMUSG00000030317: 4394 Updated longest transcript for ENSMUSG00000098348: 148 Updated longest transcript for ENSMUSG00000027868: 1636 Updated longest transcript for ENSMUSG00000095743: 774 Updated longest transcript for ENSMUSG00000021680: 566 Updated longest transcript for ENSMUSG00000021771: 642 Updated longest transcript for ENSMUSG00000043398: 1642 Updated longest transcript for ENSMUSG00000091676: 90 Updated longest transcript for ENSMUSG00000038349: 2822 Updated longest transcript for ENSMUSG00000030704: 1949 Updated longest transcript for ENSMUSG00000046826: 383 Updated longest transcript for ENSMUSG00000042474: 140 Updated longest transcript for ENSMUSG00000096485: 880 Updated longest transcript for ENSMUSG00000032298: 456 Updated longest transcript for ENSMUSG00000026596: 6852 Updated longest transcript for ENSMUSG00000030325: 1800 Updated longest transcript for ENSMUSG00000049073: 716 Updated longest transcript for ENSMUSG00000075304: 560 Updated longest transcript for ENSMUSG00000036564: 2615 Updated longest transcript for ENSMUSG00000021518: 2226 Updated longest transcript for ENSMUSG00000028687: 1066 Updated longest transcript for ENSMUSG00000020949: 710 Updated longest transcript for ENSMUSG00000057447: 5062 Updated longest transcript for ENSMUSG00000052656: 2120 Updated longest transcript for ENSMUSG00000029538: 1205 Updated longest transcript for ENSMUSG00000014686: 206 Updated longest transcript for ENSMUSG00000032224: 2256 Updated longest transcript for ENSMUSG00000059203: 322 Updated longest transcript for ENSMUSG00000031389: 3294 Updated longest transcript for ENSMUSG00000024088: 179 Updated longest transcript for ENSMUSG00000078687: 271 Updated longest transcript for ENSMUSG00000090440: 1134 Updated longest transcript for ENSMUSG00000063660: 22 Updated longest transcript for ENSMUSG00000094248: 85 Updated longest transcript for ENSMUSG00000071769: 195 Updated longest transcript for ENSMUSG00000031821: 3053 Updated longest transcript for ENSMUSG00000029407: 2070 Updated longest transcript for ENSMUSG00000029279: 1532 Updated longest transcript for ENSMUSG00000093804: 1539 Updated longest transcript for ENSMUSG00000021493: 1172 Updated longest transcript for ENSMUSG00000032502: 1968 Updated 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Updated longest transcript for ENSMUSG00000041625: 492 Updated longest transcript for ENSMUSG00000030309: 518 Updated longest transcript for ENSMUSG00000028883: 2962 Updated longest transcript for ENSMUSG00000047507: 283 Updated longest transcript for ENSMUSG00000062608: 999 Updated longest transcript for ENSMUSG00000032431: 395 Updated longest transcript for ENSMUSG00000029826: 1902 Updated longest transcript for ENSMUSG00000089865: 3196 Updated longest transcript for ENSMUSG00000041923: 1760 Updated longest transcript for ENSMUSG00000042385: 655 Updated longest transcript for ENSMUSG00000024858: 1099 Updated longest transcript for ENSMUSG00000059105: 341 Updated longest transcript for ENSMUSG00000043727: 321 Updated longest transcript for ENSMUSG00000033487: 2221 Updated longest transcript for ENSMUSG00000036617: 2315 Updated longest transcript for ENSMUSG00000074199: 139 Updated longest transcript for ENSMUSG00000004709: 783 Updated longest transcript for ENSMUSG00000032565: 999 Updated longest transcript for ENSMUSG00000050549: 1697 Updated longest transcript for ENSMUSG00000021468: 1136 Updated longest transcript for ENSMUSG00000038709: 141 Updated longest transcript for ENSMUSG00000027398: 459 Updated longest transcript for ENSMUSG00000041592: 4416 Updated longest transcript for ENSMUSG00000002578: 3205 Updated longest transcript for ENSMUSG00000043017: 1791 Updated longest transcript for ENSMUSG00000032359: 1100 Updated longest transcript for ENSMUSG00000056910: 9527 Updated longest transcript for ENSMUSG00000039000: 1523 Updated longest transcript for ENSMUSG00000074832: 399 Updated longest transcript for ENSMUSG00000030760: 10448 Updated longest transcript for ENSMUSG00000006007: 2990 Updated longest transcript for ENSMUSG00000042363: 2796 Updated longest transcript for ENSMUSG00000109835: 2966 Updated longest transcript for ENSMUSG00000038498: 49 Updated longest transcript for ENSMUSG00000031506: 1666 Updated longest transcript for ENSMUSG00000025795: 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Updated longest transcript for ENSMUSG00000079710: 1963 Updated longest transcript for ENSMUSG00000055933: 191 Updated longest transcript for ENSMUSG00000031393: 8825 Updated longest transcript for ENSMUSG00000035387: 50 Updated longest transcript for ENSMUSG00000118429: 367 Updated longest transcript for ENSMUSG00000063133: 45 Updated longest transcript for ENSMUSG00000029204: 479 Updated longest transcript for ENSMUSG00000045731: 1234 Updated longest transcript for ENSMUSG00000028681: 3082 Updated longest transcript for ENSMUSG00000029101: 1171 Updated longest transcript for ENSMUSG00000034032: 3900 Updated longest transcript for ENSMUSG00000040373: 2747 Updated longest transcript for ENSMUSG00000071652: 153 Updated longest transcript for ENSMUSG00000017716: 2936 Updated longest transcript for ENSMUSG00000020248: 1714 Updated longest transcript for ENSMUSG00000031983: 2152 Updated longest transcript for ENSMUSG00000058022: 1610 Updated longest transcript for ENSMUSG00000017400: 1343 Updated longest transcript for ENSMUSG00000042210: 171 Updated longest transcript for ENSMUSG00000058491: 674 Updated longest transcript for ENSMUSG00000053801: 1577 Updated longest transcript for ENSMUSG00000026870: 1835 Updated longest transcript for ENSMUSG00000059106: 1884 Updated longest transcript for ENSMUSG00000068917: 1346 Updated longest transcript for ENSMUSG00000095538: 1256 Updated longest transcript for ENSMUSG00000104217: 614 Updated longest transcript for ENSMUSG00000049401: 311 Updated longest transcript for ENSMUSG00000053477: 6007 Updated longest transcript for ENSMUSG00000027712: 421 Updated longest transcript for ENSMUSG00000022812: 210 Updated longest transcript for ENSMUSG00000014773: 676 Updated longest transcript for ENSMUSG00000056995: 5270 Updated longest transcript for ENSMUSG00000027111: 1310 Updated longest transcript for ENSMUSG00000029408: 1018 Updated longest transcript for ENSMUSG00000038312: 418 Updated longest transcript for ENSMUSG00000061099: 471 Updated longest transcript for ENSMUSG00000001138: 3121 Updated longest transcript for ENSMUSG00000067148: 115 Updated longest transcript for ENSMUSG00000020289: 3034 Updated longest transcript for ENSMUSG00000032977: 1689 Updated longest transcript for ENSMUSG00000028914: 2289 Updated longest transcript for ENSMUSG00000039521: 2202 Updated longest transcript for ENSMUSG00000042719: 2607 Updated longest transcript for ENSMUSG00000070423: 1850 Updated longest transcript for ENSMUSG00000017721: 2181 Updated longest transcript for ENSMUSG00000053508: 190 Updated longest transcript for ENSMUSG00000056201: 1708 Updated longest transcript for ENSMUSG00000038150: 1456 Updated longest transcript for ENSMUSG00000030335: 2850 Updated longest transcript for ENSMUSG00000071490: 46 Updated longest transcript for ENSMUSG00000061517: 1573 Updated longest transcript for ENSMUSG00000022890: 92 Updated longest transcript for ENSMUSG00000051786: 2055 Updated longest transcript for ENSMUSG00000027132: 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Updated longest transcript for ENSMUSG00000026317: 4866 Updated longest transcript for ENSMUSG00000008429: 712 Updated longest transcript for ENSMUSG00000066643: 2086 Updated longest transcript for ENSMUSG00000027612: 2318 Updated longest transcript for ENSMUSG00000094084: 1045 Updated longest transcript for ENSMUSG00000038691: 157 Updated longest transcript for ENSMUSG00000084989: 2215 Updated longest transcript for ENSMUSG00000030785: 294 Updated longest transcript for ENSMUSG00000090863: 2574 Updated longest transcript for ENSMUSG00000020876: 1491 Updated longest transcript for ENSMUSG00000014767: 11596 Updated longest transcript for ENSMUSG00000061742: 284 Updated longest transcript for ENSMUSG00000078793: 212 Updated longest transcript for ENSMUSG00000022822: 1301 Updated longest transcript for ENSMUSG00000025766: 3690 Updated longest transcript for ENSMUSG00000032340: 2587 Updated longest transcript for ENSMUSG00000066568: 1346 Updated longest transcript for ENSMUSG00000022023: 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transcript for ENSMUSG00000056608: 3581 Updated longest transcript for ENSMUSG00000024440: 1716 Updated longest transcript for ENSMUSG00000034771: 309 Updated longest transcript for ENSMUSG00000040260: 158 Updated longest transcript for ENSMUSG00000041134: 4657 Updated longest transcript for ENSMUSG00000026222: 2798 Updated longest transcript for ENSMUSG00000096045: 2120 Updated longest transcript for ENSMUSG00000006576: 3282 Updated longest transcript for ENSMUSG00000022297: 1634 Updated longest transcript for ENSMUSG00000068479: 2754 Updated longest transcript for ENSMUSG00000069793: 2786 Updated longest transcript for ENSMUSG00000099564: 435 Updated longest transcript for ENSMUSG00000045795: 760 Updated longest transcript for ENSMUSG00000033027: 364 Updated longest transcript for ENSMUSG00000060510: 3997 Updated longest transcript for ENSMUSG00000045204: 1610 Updated longest transcript for ENSMUSG00000078865: 203 Updated longest transcript for ENSMUSG00000094484: 2026 Updated longest transcript for ENSMUSG00000049551: 313 Updated longest transcript for ENSMUSG00000037509: 1480 Updated longest transcript for ENSMUSG00000032417: 248 Updated longest transcript for ENSMUSG00000028841: 274 Updated longest transcript for ENSMUSG00000070547: 2997 Updated longest transcript for ENSMUSG00000038812: 298 Updated longest transcript for ENSMUSG00000029169: 521 Updated longest transcript for ENSMUSG00000056313: 1396 Updated longest transcript for ENSMUSG00000109129: 330 Updated longest transcript for ENSMUSG00000046192: 1192 Updated longest transcript for ENSMUSG00000042485: 580 Updated longest transcript for ENSMUSG00000020437: 2670 Updated longest transcript for ENSMUSG00000093968: 763 Updated longest transcript for ENSMUSG00000040576: 268 Updated longest transcript for ENSMUSG00000004947: 1240 Updated longest transcript for ENSMUSG00000038541: 751 Updated longest transcript for ENSMUSG00000061778: 959 Updated longest transcript for ENSMUSG00000078799: 110 Updated longest transcript for ENSMUSG00000090942: 1919 Updated longest transcript for ENSMUSG00000001134: 308 Updated longest transcript for ENSMUSG00000032405: 3600 Updated longest transcript for ENSMUSG00000091311: 170 Updated longest transcript for ENSMUSG00000061048: 1847 Updated longest transcript for ENSMUSG00000022911: 1947 Updated longest transcript for ENSMUSG00000094825: 1133 Updated longest transcript for ENSMUSG00000032411: 5675 Updated longest transcript for ENSMUSG00000054434: 992 Updated longest transcript for ENSMUSG00000110591: 1292 Updated longest transcript for ENSMUSG00000024587: 2077 Updated longest transcript for ENSMUSG00000075151: 67 Updated longest transcript for ENSMUSG00000038257: 4718 Updated longest transcript for ENSMUSG00000026100: 1782 Updated longest transcript for ENSMUSG00000031532: 724 Updated longest transcript for ENSMUSG00000073664: 7336 Updated longest transcript for ENSMUSG00000020657: 3672 Updated longest transcript for ENSMUSG00000029070: 758 Updated longest transcript for ENSMUSG00000026102: 4241 Updated longest transcript for ENSMUSG00000029231: 3115 Updated longest transcript for ENSMUSG00000020609: 938 Updated longest transcript for ENSMUSG00000001168: 560 Updated longest transcript for ENSMUSG00000034584: 11 Updated longest transcript for ENSMUSG00000031378: 1003 Updated longest transcript for ENSMUSG00000020072: 1060 Updated longest transcript for ENSMUSG00000030589: 2459 Updated longest transcript for ENSMUSG00000060560: 589 Updated longest transcript for ENSMUSG00000038540: 2667 Updated longest transcript for ENSMUSG00000031073: 1005 Updated longest transcript for ENSMUSG00000051998: 687 Updated longest transcript for ENSMUSG00000027808: 4011 Updated longest transcript for ENSMUSG00000020333: 3559 Updated longest transcript for ENSMUSG00000015023: 1033 Updated longest transcript for ENSMUSG00000058952: 194 Updated longest transcript for ENSMUSG00000022113: 1661 Updated longest transcript for ENSMUSG00000053870: 1698 Updated longest transcript for ENSMUSG00000095075: 635 Updated longest transcript for ENSMUSG00000017049: 174 Updated longest transcript for ENSMUSG00000052752: 2298 Updated longest transcript for ENSMUSG00000094893: 522 Updated longest transcript for ENSMUSG00000000214: 850 Updated longest transcript for ENSMUSG00000062867: 65 Updated longest transcript for ENSMUSG00000038246: 76 Updated longest transcript for ENSMUSG00000036916: 1676 Updated longest transcript for ENSMUSG00000033831: 2262 Updated longest transcript for ENSMUSG00000026343: 978 Updated longest transcript for ENSMUSG00000061845: 88 Updated longest transcript for ENSMUSG00000008958: 273 Updated longest transcript for ENSMUSG00000023176: 2860 Updated longest transcript for ENSMUSG00000052727: 4360 Updated longest transcript for ENSMUSG00000035067: 3686 Updated longest transcript for ENSMUSG00000028702: 245 Updated longest transcript for ENSMUSG00000033080: 2299 Updated longest transcript for ENSMUSG00000079396: 1318 Updated longest transcript for ENSMUSG00000060121: 694 Updated longest transcript for ENSMUSG00000032402: 3495 Updated longest transcript for ENSMUSG00000031661: 2732 Updated longest transcript for ENSMUSG00000029433: 2649 Updated longest transcript for ENSMUSG00000049972: 340 Updated longest transcript for ENSMUSG00000021048: 227 Updated longest transcript for ENSMUSG00000047822: 260 Updated longest transcript for ENSMUSG00000073598: 2021 Updated longest transcript for ENSMUSG00000000204: 922 Updated longest transcript for ENSMUSG00000008348: 92 Updated longest transcript for ENSMUSG00000006398: 178 Updated longest transcript for ENSMUSG00000029408: 1133 Updated longest transcript for ENSMUSG00000026313: 4568 Updated longest transcript for ENSMUSG00000059406: 152 Updated longest transcript for ENSMUSG00000031608: 2286 Updated longest transcript for ENSMUSG00000034472: 1865 Updated longest transcript for ENSMUSG00000026721: 5042 Updated longest transcript for ENSMUSG00000045624: 729 Updated longest transcript for ENSMUSG00000036810: 1843 Updated longest transcript for ENSMUSG00000100254: 145 Updated longest transcript for ENSMUSG00000031176: 1720 Updated longest transcript for ENSMUSG00000061462: 3490 Updated longest transcript for ENSMUSG00000030983: 268 Updated longest transcript for ENSMUSG00000021062: 2283 Updated longest transcript for ENSMUSG00000031290: 2582 Updated longest transcript for ENSMUSG00000019214: 2289 Updated longest transcript for ENSMUSG00000015488: 2111 Updated longest transcript for ENSMUSG00000072780: 9369 Updated longest transcript for ENSMUSG00000024579: 475 Updated longest transcript for ENSMUSG00000052160: 368 Updated longest transcript for ENSMUSG00000038708: 902 Updated longest transcript for ENSMUSG00000051258: 63 Updated longest transcript for ENSMUSG00000020952: 1571 Updated longest transcript for ENSMUSG00000020177: 654 Updated longest transcript for ENSMUSG00000025324: 683 Updated longest transcript for ENSMUSG00000040724: 9401 Updated longest transcript for ENSMUSG00000041117: 871 Updated longest transcript for ENSMUSG00000078324: 151 Updated longest transcript for ENSMUSG00000024780: 4133 Updated longest transcript for ENSMUSG00000023289: 146 Updated longest transcript for ENSMUSG00000026554: 1717 Updated longest transcript for ENSMUSG00000116486: 3492 Updated longest transcript for ENSMUSG00000030703: 18 Updated longest transcript for ENSMUSG00000028799: 1433 Updated longest transcript for ENSMUSG00000035781: 987 Updated longest transcript for ENSMUSG00000068600: 235 Updated longest transcript for ENSMUSG00000006920: 1824 Updated longest transcript for ENSMUSG00000073062: 2891 Updated longest transcript for ENSMUSG00000014846: 343 Updated longest transcript for ENSMUSG00000018589: 1267 Updated longest transcript for ENSMUSG00000032782: 2504 Updated longest transcript for ENSMUSG00000025403: 666 Updated longest transcript for ENSMUSG00000048540: 2431 Updated longest transcript for ENSMUSG00000020312: 1453 Updated longest transcript for ENSMUSG00000008976: 3176 Completed parsing ARE_score_data/3primeUTR_AREscore_GRC38p6_full.txt. Total genes: 21458 Parsing ARE score data from ARE_score_data/3primeUTR_AREscore_GRC39_full.txt... Warning: No sequence found for ENSMUST00000249497 Warning: No sequence found for ENSMUST00000249279 Updated longest transcript for ENSMUSG00000086587: 386 Warning: No sequence found for ENSMUST00000249374 Updated longest transcript for ENSMUSG00000096574: 1313 Warning: No sequence found for ENSMUST00000249865 Warning: No sequence found for ENSMUST00000249689 Warning: No sequence found for ENSMUST00000247756 Updated longest transcript for ENSMUSG00000079593: 260 Warning: No sequence found for ENSMUST00000249593 Warning: No sequence found for ENSMUST00000249921 Updated longest transcript for ENSMUSG00000078898: 5033 Warning: No sequence found for ENSMUST00000249392 Updated longest transcript for ENSMUSG00000090015: 547 Updated longest transcript for ENSMUSG00000109156: 101 Warning: No sequence found for ENSMUST00000246835 Updated longest transcript for ENSMUSG00000086587: 391 Warning: No sequence found for ENSMUST00000177994 Updated longest transcript for ENSMUSG00000121587: 120 Updated longest transcript for ENSMUSG00000078901: 3470 Warning: No sequence found for ENSMUST00000239509 Updated longest transcript for ENSMUSG00000118664: 1546 Updated longest transcript for ENSMUSG00000078487: 20 Warning: No sequence found for ENSMUST00000239544 Updated longest transcript for ENSMUSG00000079418: 1198 Warning: No sequence found for ENSMUST00000242469 Updated longest transcript for ENSMUSG00000120166: 1356 Warning: No sequence found for ENSMUST00000231152 Updated longest transcript for ENSMUSG00000121584: 247 Warning: No sequence found for ENSMUST00000249298 Updated longest transcript for ENSMUSG00000074529: 532 Warning: No sequence found for ENSMUST00000249872 Warning: No sequence found for ENSMUST00000249889 Updated longest transcript for ENSMUSG00000034354: 1901 Warning: No sequence found for ENSMUST00000249902 Updated longest transcript for ENSMUSG00000020439: 327 Warning: No sequence found for ENSMUST00000249597 Warning: No sequence found for ENSMUST00000249851 Updated longest transcript for ENSMUSG00000121908: 303 Updated longest transcript for ENSMUSG00000079593: 273 Warning: No sequence found for ENSMUST00000249493 Updated longest transcript for ENSMUSG00000091477: 804 Warning: No sequence found for ENSMUST00000249586 Updated longest transcript for ENSMUSG00000050844: 652 Warning: No sequence found for ENSMUST00000105569 Updated longest transcript for ENSMUSG00002076083: 699 Warning: No sequence found for ENSMUST00000249775 Updated longest transcript for ENSMUSG00000050824: 1292 Warning: No sequence found for ENSMUST00000249329 Updated longest transcript for ENSMUSG00000030284: 967 Warning: No sequence found for ENSMUST00000240553 Warning: No sequence found for ENSMUST00000249899 Updated longest transcript for ENSMUSG00000029409: 135 Warning: No sequence found for ENSMUST00000239563 Warning: No sequence found for ENSMUST00000249364 Updated longest transcript for ENSMUSG00000007682: 4872 Warning: No sequence found for ENSMUST00000249742 Warning: No sequence found for ENSMUST00000239547 Updated longest transcript for ENSMUSG00000118669: 1477 Updated longest transcript for ENSMUSG00000118430: 655 Warning: No sequence found for ENSMUST00000249743 Warning: No sequence found for ENSMUST00000249682 Warning: No sequence found for ENSMUST00000249546 Updated longest transcript for ENSMUSG00000095318: 773 Warning: No sequence found for ENSMUST00000249544 Updated longest transcript for ENSMUSG00000095318: 780 Warning: No sequence found for ENSMUST00000249708 Warning: No sequence found for ENSMUST00000249418 Updated longest transcript for ENSMUSG00000072598: 162 Warning: No sequence found for ENSMUST00000240759 Updated longest transcript for ENSMUSG00000094475: 807 Warning: No sequence found for ENSMUST00000249600 Warning: No sequence found for ENSMUST00000249283 Warning: No sequence found for ENSMUST00000249932 Updated longest transcript for ENSMUSG00000095648: 362 Updated longest transcript for ENSMUSG00000036322: 355 Warning: No sequence found for ENSMUST00000249694 Updated longest transcript for ENSMUSG00000015120: 1918 Warning: No sequence found for ENSMUST00000249316 Warning: No sequence found for ENSMUST00000241714 Warning: No sequence found for ENSMUST00000249668 Updated longest transcript for ENSMUSG00000121607: 1326 Updated longest transcript for ENSMUSG00000063171: 435 Warning: No sequence found for ENSMUST00000249501 Warning: No sequence found for ENSMUST00000249924 Updated longest transcript for ENSMUSG00000036779: 2450 Warning: No sequence found for ENSMUST00000240708 Updated longest transcript for ENSMUSG00000114075: 1073 Warning: No sequence found for ENSMUST00000249914 Updated longest transcript for ENSMUSG00000011350: 1365 Warning: No sequence found for ENSMUST00000249333 Warning: No sequence found for ENSMUST00000249701 Updated longest transcript for ENSMUSG00000055114: 403 Warning: No sequence found for ENSMUST00020183810 Warning: No sequence found for ENSMUST00000249337 Updated longest transcript for ENSMUSG00000020633: 983 Warning: No sequence found for ENSMUST00000249702 Warning: No sequence found for ENSMUST00000249378 Updated longest transcript for ENSMUSG00000096793: 1603 Updated longest transcript for ENSMUSG00000079941: 79 Warning: No sequence found for ENSMUST00000249370 Warning: No sequence found for ENSMUST00000249291 Warning: No sequence found for ENSMUST00000249842 Updated longest transcript for ENSMUSG00000015126: 879 Warning: No sequence found for ENSMUST00000240540 Updated longest transcript for ENSMUSG00000113805: 932 Warning: No sequence found for ENSMUST00000249863 Warning: No sequence found for ENSMUST00000249730 Warning: No sequence found for ENSMUST00000249732 Warning: No sequence found for ENSMUST00000249321 Updated longest transcript for ENSMUSG00000074529: 600 Warning: No sequence found for ENSMUST00000249663 Updated longest transcript for ENSMUSG00000121605: 119 Warning: No sequence found for ENSMUST00000249068 Warning: No sequence found for ENSMUST00000249287 Updated longest transcript for ENSMUSG00000022253: 2534 Updated longest transcript for ENSMUSG00000024176: 1673 Warning: No sequence found for ENSMUST00000249257 Updated longest transcript for ENSMUSG00000075023: 483 Warning: No sequence found for ENSMUST00000249923 Updated longest transcript for ENSMUSG00000073001: 107 Warning: No sequence found for ENSMUST00000249750 Warning: No sequence found for ENSMUST00000249695 Updated longest transcript for ENSMUSG00000063277: 1139 Warning: No sequence found for ENSMUST00000249885 Updated longest transcript for ENSMUSG00000062691: 1072 Warning: No sequence found for ENSMUST00000240548 Warning: No sequence found for ENSMUST00000249876 Warning: No sequence found for ENSMUST00000240547 Updated longest transcript for ENSMUSG00000000386: 1647 Warning: No sequence found for ENSMUST00000249221 Updated longest transcript for ENSMUSG00000094091: 344 Warning: No sequence found for ENSMUST00000249773 Warning: No sequence found for ENSMUST00000247757 Warning: No sequence found for ENSMUST00000240713 Updated longest transcript for ENSMUSG00000092335: 2252 Warning: No sequence found for ENSMUST00000249607 Updated longest transcript for ENSMUSG00000094132: 1057 Updated longest transcript for ENSMUSG00000116376: 123 Warning: No sequence found for ENSMUST00000240719 Warning: No sequence found for ENSMUST00000247219 Warning: No sequence found for ENSMUST00000249267 Updated longest transcript for ENSMUSG00000073448: 444 Warning: No sequence found for ENSMUST00010181660 Updated longest transcript for ENSMUSG00001074846: 53 Warning: No sequence found for ENSMUST00000249683 Warning: No sequence found for ENSMUST00000249361 Updated longest transcript for ENSMUSG00000095648: 462 Warning: No sequence found for ENSMUST00020183806 Updated longest transcript for ENSMUSG00000062691: 1074 Warning: No sequence found for ENSMUST00010126032 Updated longest transcript for ENSMUSG00001074846: 460 Warning: No sequence found for ENSMUST00000240709 Warning: No sequence found for ENSMUST00000249302 Updated longest transcript for ENSMUSG00000015120: 1919 Warning: No sequence found for ENSMUST00000249933 Updated longest transcript for ENSMUSG00000118415: 2396 Warning: No sequence found for ENSMUST00000249918 Warning: No sequence found for ENSMUST00000240546 Warning: No sequence found for ENSMUST00000249434 Warning: No sequence found for ENSMUST00000239500 Updated longest transcript for ENSMUSG00000118661: 589 Warning: No sequence found for ENSMUST00000249858 Updated longest transcript for ENSMUSG00000093806: 342 Warning: No sequence found for ENSMUST00000249891 Warning: No sequence found for ENSMUST00000249388 Updated longest transcript for ENSMUSG00000121596: 1420 Warning: No sequence found for ENSMUST00000239562 Warning: No sequence found for ENSMUST00000248980 Warning: No sequence found for ENSMUST00000248072 Warning: No sequence found for ENSMUST00000240716 Warning: No sequence found for ENSMUST00000239529 Updated longest transcript for ENSMUSG00000118668: 190 Warning: No sequence found for ENSMUST00000249864 Warning: No sequence found for ENSMUST00000249379 Warning: No sequence found for ENSMUST00000249747 Warning: No sequence found for ENSMUST00000240758 Warning: No sequence found for ENSMUST00000247844 Warning: No sequence found for ENSMUST00000249247 Warning: No sequence found for ENSMUST00000249919 Warning: No sequence found for ENSMUST00000239519 Updated longest transcript for ENSMUSG00000073791: 1655 Warning: No sequence found for ENSMUST00000249613 Warning: No sequence found for ENSMUST00000249705 Warning: No sequence found for ENSMUST00000249277 Updated longest transcript for ENSMUSG00000094932: 869 Warning: No sequence found for ENSMUST00000239551 Warning: No sequence found for ENSMUST00020183325 Updated longest transcript for ENSMUSG00002076083: 2117 Warning: No sequence found for ENSMUST00000249274 Updated longest transcript for ENSMUSG00000028883: 3217 Warning: No sequence found for ENSMUST00000240706 Warning: No sequence found for ENSMUST00000249584 Updated longest transcript for ENSMUSG00000096619: 567 Warning: No sequence found for ENSMUST00000249866 Updated longest transcript for ENSMUSG00000095799: 928 Updated longest transcript for ENSMUSG00000024171: 206 Warning: No sequence found for ENSMUST00000249241 Updated longest transcript for ENSMUSG00000121570: 8 Warning: No sequence found for ENSMUST00000239536 Updated longest transcript for ENSMUSG00000118669: 1482 Warning: No sequence found for ENSMUST00000249913 Updated longest transcript for ENSMUSG00000029447: 551 Warning: No sequence found for ENSMUST00000249862 Updated longest transcript for ENSMUSG00000008658: 1836 Updated longest transcript for ENSMUSG00000118516: 2774 Warning: No sequence found for ENSMUST00000249860 Updated longest transcript for ENSMUSG00000079164: 691 Updated longest transcript for ENSMUSG00000114591: 729 Warning: No sequence found for ENSMUST00000249368 Warning: No sequence found for ENSMUST00000189062 Updated longest transcript for ENSMUSG00000121918: 1338 Warning: No sequence found for ENSMUST00000249868 Updated longest transcript for ENSMUSG00000058183: 1686 Updated longest transcript for ENSMUSG00000015451: 105 Warning: No sequence found for ENSMUST00000249699 Warning: No sequence found for ENSMUST00000239534 Warning: No sequence found for ENSMUST00000249567 Updated longest transcript for ENSMUSG00000091568: 1136 Updated longest transcript for ENSMUSG00000074521: 3609 Warning: No sequence found for ENSMUST00000249348 Warning: No sequence found for ENSMUST00000249925 Warning: No sequence found for ENSMUST00000249713 Warning: No sequence found for ENSMUST00000249620 Warning: No sequence found for ENSMUST00000249908 Updated longest transcript for ENSMUSG00000053253: 1768 Warning: No sequence found for ENSMUST00020183803 Warning: No sequence found for ENSMUST00000249608 Warning: No sequence found for ENSMUST00000249313 Warning: No sequence found for ENSMUST00000249494 Updated longest transcript for ENSMUSG00000091477: 811 Warning: No sequence found for ENSMUST00000249935 Updated longest transcript for ENSMUSG00000121920: 264 Warning: No sequence found for ENSMUST00000249827 Warning: No sequence found for ENSMUST00000249455 Updated longest transcript for ENSMUSG00000017688: 2624 Updated longest transcript for ENSMUSG00000049133: 598 Warning: No sequence found for ENSMUST00000185223 Updated longest transcript for ENSMUSG00000023259: 1694 Warning: No sequence found for ENSMUST00000240551 Updated longest transcript for ENSMUSG00000027157: 1215 Warning: No sequence found for ENSMUST00000239516 Warning: No sequence found for ENSMUST00000249300 Warning: No sequence found for ENSMUST00000249293 Updated longest transcript for ENSMUSG00000095474: 2391 Warning: No sequence found for ENSMUST00000249372 Updated longest transcript for ENSMUSG00000091110: 1688 Warning: No sequence found for ENSMUST00000240589 Warning: No sequence found for ENSMUST00000249502 Updated longest transcript for ENSMUSG00000004677: 1255 Warning: No sequence found for ENSMUST00000249320 Warning: No sequence found for ENSMUST00000249395 Warning: No sequence found for ENSMUST00000249363 Warning: No sequence found for ENSMUST00000249366 Warning: No sequence found for ENSMUST00000249308 Warning: No sequence found for ENSMUST00000249898 Warning: No sequence found for ENSMUST00000249290 Updated longest transcript for ENSMUSG00000024942: 817 Warning: No sequence found for ENSMUST00000249765 Warning: No sequence found for ENSMUST00000249458 Warning: No sequence found for ENSMUST00000249304 Updated longest transcript for ENSMUSG00000095180: 117 Warning: No sequence found for ENSMUST00000249389 Warning: No sequence found for ENSMUST00000249621 Updated longest transcript for ENSMUSG00000032846: 1818 Warning: No sequence found for ENSMUST00000249761 Updated longest transcript for ENSMUSG00000121905: 115 Warning: No sequence found for ENSMUST00000240714 Warning: No sequence found for ENSMUST00000247843 Warning: No sequence found for ENSMUST00000249431 Warning: No sequence found for ENSMUST00000249833 Warning: No sequence found for ENSMUST00000249697 Warning: No sequence found for ENSMUST00000249612 Warning: No sequence found for ENSMUST00000247868 Updated longest transcript for ENSMUSG00000026321: 3117 Updated longest transcript for ENSMUSG00000036073: 429 Warning: No sequence found for ENSMUST00000239521 Warning: No sequence found for ENSMUST00000249631 Updated longest transcript for ENSMUSG00000110218: 1691 Warning: No sequence found for ENSMUST00000249401 Updated longest transcript for ENSMUSG00000114582: 729 Warning: No sequence found for ENSMUST00000249849 Updated longest transcript for ENSMUSG00000082226: 368 Warning: No sequence found for ENSMUST00000153071 Updated longest transcript for ENSMUSG00000121906: 337 Warning: No sequence found for ENSMUST00000240549 Warning: No sequence found for ENSMUST00000249382 Updated longest transcript for ENSMUSG00000095195: 1134 Warning: No sequence found for ENSMUST00000249299 Warning: No sequence found for ENSMUST00000239541 Updated longest transcript for ENSMUSG00000073991: 35 Warning: No sequence found for ENSMUST00000249276 Warning: No sequence found for ENSMUST00000249305 Warning: No sequence found for ENSMUST00000249762 Updated longest transcript for ENSMUSG00000024175: 859 Warning: No sequence found for ENSMUST00000249585 Warning: No sequence found for ENSMUST00000075040 Updated longest transcript for ENSMUSG00000121906: 1150 Warning: No sequence found for ENSMUST00000239561 Warning: No sequence found for ENSMUST00000249110 Warning: No sequence found for ENSMUST00000249691 Warning: No sequence found for ENSMUST00000249376 Warning: No sequence found for ENSMUST00000249763 Warning: No sequence found for ENSMUST00000249602 Warning: No sequence found for ENSMUST00000249895 Warning: No sequence found for ENSMUST00000249436 Warning: No sequence found for ENSMUST00000249617 Warning: No sequence found for ENSMUST00000240715 Warning: No sequence found for ENSMUST00000249703 Updated longest transcript for ENSMUSG00000026285: 1003 Warning: No sequence found for ENSMUST00000249551 Warning: No sequence found for ENSMUST00000249355 Warning: No sequence found for ENSMUST00000249594 Warning: No sequence found for ENSMUST00000249384 Updated longest transcript for ENSMUSG00000031838: 201 Warning: No sequence found for ENSMUST00000249475 Updated longest transcript for ENSMUSG00000069355: 551 Warning: No sequence found for ENSMUST00000249879 Updated longest transcript for ENSMUSG00000031714: 1895 Warning: No sequence found for ENSMUST00000249867 Warning: No sequence found for ENSMUST00000239525 Updated longest transcript for ENSMUSG00000118667: 712 Warning: No sequence found for ENSMUST00000249256 Warning: No sequence found for ENSMUST00000249285 Updated longest transcript for ENSMUSG00000092572: 2199 Warning: No sequence found for ENSMUST00000240702 Updated longest transcript for ENSMUSG00000013083: 156 Warning: No sequence found for ENSMUST00000249433 Updated longest transcript for ENSMUSG00000096803: 366 Warning: No sequence found for ENSMUST00000249598 Warning: No sequence found for ENSMUST00000249258 Updated longest transcript for ENSMUSG00000121585: 123 Warning: No sequence found for ENSMUST00000249319 Updated longest transcript for ENSMUSG00000095545: 7392 Warning: No sequence found for ENSMUST00000249604 Updated longest transcript for ENSMUSG00000094132: 1059 Updated longest transcript for ENSMUSG00000085549: 222 Warning: No sequence found for ENSMUST00000249733 Warning: No sequence found for ENSMUST00000239545 Updated longest transcript for ENSMUSG00000118670: 184 Warning: No sequence found for ENSMUST00000249934 Updated longest transcript for ENSMUSG00000113971: 929 Updated longest transcript for ENSMUSG00000034039: 178 Warning: No sequence found for ENSMUST00000249595 Warning: No sequence found for ENSMUST00000247846 Warning: No sequence found for ENSMUST00000249432 Warning: No sequence found for ENSMUST00000249120 Updated longest transcript for ENSMUSG00000086158: 173 Warning: No sequence found for ENSMUST00000249296 Warning: No sequence found for ENSMUST00000249312 Warning: No sequence found for ENSMUST00000249097 Updated longest transcript for ENSMUSG00000073991: 399 Warning: No sequence found for ENSMUST00000249656 Updated longest transcript for ENSMUSG00000020894: 2092 Warning: No sequence found for ENSMUST00000239552 Updated longest transcript for ENSMUSG00000118671: 822 Updated longest transcript for ENSMUSG00000024942: 818 Updated longest transcript for ENSMUSG00000053961: 162 Warning: No sequence found for ENSMUST00000249263 Warning: No sequence found for ENSMUST00000249073 Warning: No sequence found for ENSMUST00000249615 Warning: No sequence found for ENSMUST00000249233 Updated longest transcript for ENSMUSG00000024729: 123 Warning: No sequence found for ENSMUST00000249622 Warning: No sequence found for ENSMUST00000249237 Updated longest transcript for ENSMUSG00000024729: 688 Warning: No sequence found for ENSMUST00000249828 Warning: No sequence found for ENSMUST00000249774 Updated longest transcript for ENSMUSG00000078867: 2802 Warning: No sequence found for ENSMUST00000249476 Updated longest transcript for ENSMUSG00000056938: 3466 Warning: No sequence found for ENSMUST00000249861 Updated longest transcript for ENSMUSG00000072762: 1238 Warning: No sequence found for ENSMUST00000249870 Updated longest transcript for ENSMUSG00000090093: 1072 Warning: No sequence found for ENSMUST00000249706 Warning: No sequence found for ENSMUST00000249840 Warning: No sequence found for ENSMUST00000249338 Updated longest transcript for ENSMUSG00000020633: 990 Updated longest transcript for ENSMUSG00000070686: 1493 Warning: No sequence found for ENSMUST00000249231 Warning: No sequence found for ENSMUST00000249552 Warning: No sequence found for ENSMUST00000249457 Updated longest transcript for ENSMUSG00000094021: 647 Warning: No sequence found for ENSMUST00000239523 Updated longest transcript for ENSMUSG00000095180: 120 Warning: No sequence found for ENSMUST00000249909 Updated longest transcript for ENSMUSG00000096049: 922 Warning: No sequence found for ENSMUST00000249232 Warning: No sequence found for ENSMUST00000249242 Updated longest transcript for ENSMUSG00000121571: 662 Warning: No sequence found for ENSMUST00000249230 Warning: No sequence found for ENSMUST00000249369 Warning: No sequence found for ENSMUST00000249739 Warning: No sequence found for ENSMUST00000249538 Updated longest transcript for ENSMUSG00000094157: 759 Warning: No sequence found for ENSMUST00000249286 Warning: No sequence found for ENSMUST00000249549 Warning: No sequence found for ENSMUST00000240707 Warning: No sequence found for ENSMUST00000249259 Updated longest transcript for ENSMUSG00000100679: 1651 Warning: No sequence found for ENSMUST00000249720 Warning: No sequence found for ENSMUST00000249474 Updated longest transcript for ENSMUSG00000091617: 1318 Warning: No sequence found for ENSMUST00000249704 Warning: No sequence found for ENSMUST00000249331 Warning: No sequence found for ENSMUST00000247847 Updated longest transcript for ENSMUSG00000107539: 492 Warning: No sequence found for ENSMUST00010126065 Warning: No sequence found for ENSMUST00000249547 Warning: No sequence found for ENSMUST00000249603 Warning: No sequence found for ENSMUST00000249235 Warning: No sequence found for ENSMUST00000249317 Updated longest transcript for ENSMUSG00000095936: 1112 Updated longest transcript for ENSMUSG00000017466: 2638 Warning: No sequence found for ENSMUST00000249871 Warning: No sequence found for ENSMUST00000249377 Warning: No sequence found for ENSMUST00000249661 Warning: No sequence found for ENSMUST00000249303 Warning: No sequence found for ENSMUST00000239527 Updated longest transcript for ENSMUSG00000118668: 729 Warning: No sequence found for ENSMUST00000249495 Warning: No sequence found for ENSMUST00000247845 Warning: No sequence found for ENSMUST00000240711 Warning: No sequence found for ENSMUST00020183804 Warning: No sequence found for ENSMUST00000240710 Warning: No sequence found for ENSMUST00000240543 Warning: No sequence found for ENSMUST00000249043 Updated longest transcript for ENSMUSG00000094443: 115 Warning: No sequence found for ENSMUST00000212885 Updated longest transcript for ENSMUSG00000121369: 183 Updated longest transcript for ENSMUSG00000078510: 577 Warning: No sequence found for ENSMUST00000249244 Updated longest transcript for ENSMUSG00000056399: 166 Warning: No sequence found for ENSMUST00000249109 Warning: No sequence found for ENSMUST00000249915 Updated longest transcript for ENSMUSG00000060913: 1105 Warning: No sequence found for ENSMUST00000249721 Warning: No sequence found for ENSMUST00000249881 Warning: No sequence found for ENSMUST00000240539 Warning: No sequence found for ENSMUST00000247866 Warning: No sequence found for ENSMUST00000249074 Updated longest transcript for ENSMUSG00000073085: 206 Warning: No sequence found for ENSMUST00000249498 Warning: No sequence found for ENSMUST00000249890 Warning: No sequence found for ENSMUST00000249901 Warning: No sequence found for ENSMUST00000249671 Warning: No sequence found for ENSMUST00000249907 Warning: No sequence found for ENSMUST00000249681 Warning: No sequence found for ENSMUST00000249469 Warning: No sequence found for ENSMUST00000249236 Warning: No sequence found for ENSMUST00000249289 Warning: No sequence found for ENSMUST00000249133 Warning: No sequence found for ENSMUST00000249712 Warning: No sequence found for ENSMUST00000249666 Updated longest transcript for ENSMUSG00000121606: 216 Warning: No sequence found for ENSMUST00000249568 Warning: No sequence found for ENSMUST00000249606 Warning: No sequence found for ENSMUST00000249096 Warning: No sequence found for ENSMUST00000249278 Warning: No sequence found for ENSMUST00000249234 Updated longest transcript for ENSMUSG00000114694: 729 Warning: No sequence found for ENSMUST00000249288 Warning: No sequence found for ENSMUST00000239538 Warning: No sequence found for ENSMUST00000239514 Updated longest transcript for ENSMUSG00000118665: 3265 Warning: No sequence found for ENSMUST00000249931 Warning: No sequence found for ENSMUST00000249619 Warning: No sequence found for ENSMUST00010181659 Warning: No sequence found for ENSMUST00000249078 Updated longest transcript for ENSMUSG00000090379: 768 Updated longest transcript for ENSMUSG00000048424: 2330 Warning: No sequence found for ENSMUST00000249387 Warning: No sequence found for ENSMUST00000249900 Updated longest transcript for ENSMUSG00000041361: 2652 Warning: No sequence found for ENSMUST00000248712 Updated longest transcript for ENSMUSG00000121513: 4113 Warning: No sequence found for ENSMUST00000240542 Warning: No sequence found for ENSMUST00000249897 Warning: No sequence found for ENSMUST00000249496 Warning: No sequence found for ENSMUST00000249926 Warning: No sequence found for ENSMUST00000249292 Updated longest transcript for ENSMUSG00000118483: 2356 Warning: No sequence found for ENSMUST00000249835 Warning: No sequence found for ENSMUST00000249917 Warning: No sequence found for ENSMUST00000239513 Updated longest transcript for ENSMUSG00000078898: 7392 Warning: No sequence found for ENSMUST00000249269 Updated longest transcript for ENSMUSG00000073448: 683 Updated longest transcript for ENSMUSG00000060244: 443 Warning: No sequence found for ENSMUST00000249687 Updated longest transcript for ENSMUSG00000015120: 2030 Warning: No sequence found for ENSMUST00000249837 Warning: No sequence found for ENSMUST00000177359 Warning: No sequence found for ENSMUST00000249859 Updated longest transcript for ENSMUSG00000036879: 2105 Warning: No sequence found for ENSMUST00000249875 Warning: No sequence found for ENSMUST00000249735 Updated longest transcript for ENSMUSG00000033825: 801 Updated longest transcript for ENSMUSG00000023572: 512 Warning: No sequence found for ENSMUST00000249692 Warning: No sequence found for ENSMUST00000249610 Warning: No sequence found for ENSMUST00000249834 Warning: No sequence found for ENSMUST00000249281 Warning: No sequence found for ENSMUST00000239554 Updated longest transcript for ENSMUSG00000118672: 205 Warning: No sequence found for ENSMUST00000249737 Warning: No sequence found for ENSMUST00000249381 Warning: No sequence found for ENSMUST00000249250 Updated longest transcript for ENSMUSG00000121577: 666 Warning: No sequence found for ENSMUST00000249831 Warning: No sequence found for ENSMUST00000239506 Updated longest transcript for ENSMUSG00000118663: 56 Warning: No sequence found for ENSMUST00000249310 Warning: No sequence found for ENSMUST00000249350 Warning: No sequence found for ENSMUST00000248112 Updated longest transcript for ENSMUSG00000091122: 770 Warning: No sequence found for ENSMUST00020183129 Warning: No sequence found for ENSMUST00000249280 Warning: No sequence found for ENSMUST00000240721 Warning: No sequence found for ENSMUST00000249611 Updated longest transcript for ENSMUSG00000094132: 1308 Warning: No sequence found for ENSMUST00000249740 Updated longest transcript for ENSMUSG00000028108: 94 Updated longest transcript for ENSMUSG00000049608: 2557 Warning: No sequence found for ENSMUST00000249841 Warning: No sequence found for ENSMUST00000249772 Updated longest transcript for ENSMUSG00000064043: 2727 Warning: No sequence found for ENSMUST00000249839 Warning: No sequence found for ENSMUST00000249892 Warning: No sequence found for ENSMUST00000249327 Warning: No sequence found for ENSMUST00000239508 Warning: No sequence found for ENSMUST00000247875 Warning: No sequence found for ENSMUST00000249371 Updated longest transcript for ENSMUSG00000025716: 309 Warning: No sequence found for ENSMUST00000249284 Warning: No sequence found for ENSMUST00000249295 Warning: No sequence found for ENSMUST00000249545 Updated longest transcript for ENSMUSG00000095318: 1159 Warning: No sequence found for ENSMUST00000249471 Warning: No sequence found for ENSMUST00000249887 Warning: No sequence found for ENSMUST00000249764 Warning: No sequence found for ENSMUST00000248537 Updated longest transcript for ENSMUSG00000121513: 4168 Warning: No sequence found for ENSMUST00000249380 Warning: No sequence found for ENSMUST00000249477 Warning: No sequence found for ENSMUST00000249282 Warning: No sequence found for ENSMUST00000249403 Warning: No sequence found for ENSMUST00000249470 Warning: No sequence found for ENSMUST00000249880 Warning: No sequence found for ENSMUST00000249711 Warning: No sequence found for ENSMUST00000249228 Updated longest transcript for ENSMUSG00000055555: 586 Warning: No sequence found for ENSMUST00020183805 Warning: No sequence found for ENSMUST00000249684 Warning: No sequence found for ENSMUST00000239510 Warning: No sequence found for ENSMUST00000249709 Warning: No sequence found for ENSMUST00000249266 Warning: No sequence found for ENSMUST00000249349 Updated longest transcript for ENSMUSG00000029673: 2103 Warning: No sequence found for ENSMUST00000247867 Warning: No sequence found for ENSMUST00000249294 Updated longest transcript for ENSMUSG00000093701: 711 Warning: No sequence found for ENSMUST00000249306 Updated longest transcript for ENSMUSG00000105888: 126 Warning: No sequence found for ENSMUST00000240717 Updated longest transcript for ENSMUSG00000080152: 316 Warning: No sequence found for ENSMUST00000066677 Warning: No sequence found for ENSMUST00000249710 Warning: No sequence found for ENSMUST00000249857 Warning: No sequence found for ENSMUST00000249698 Warning: No sequence found for ENSMUST00000240722 Warning: No sequence found for ENSMUST00000249894 Warning: No sequence found for ENSMUST00000240671 Warning: No sequence found for ENSMUST00000249693 Warning: No sequence found for ENSMUST00000249669 Updated longest transcript for ENSMUSG00000121607: 2395 Warning: No sequence found for ENSMUST00000239535 Warning: No sequence found for ENSMUST00000239533 Warning: No sequence found for ENSMUST00000249850 Updated longest transcript for ENSMUSG00000068686: 986 Warning: No sequence found for ENSMUST00000249273 Warning: No sequence found for ENSMUST00000249307 Warning: No sequence found for ENSMUST00000249719 Warning: No sequence found for ENSMUST00000239501 Updated longest transcript for ENSMUSG00000118662: 506 Warning: No sequence found for ENSMUST00000249751 Warning: No sequence found for ENSMUST00000249700 Warning: No sequence found for ENSMUST00000249728 Warning: No sequence found for ENSMUST00000239548 Warning: No sequence found for ENSMUST00000240720 Warning: No sequence found for ENSMUST00000249729 Updated longest transcript for ENSMUSG00000002111: 619 Warning: No sequence found for ENSMUST00000249565 Warning: No sequence found for ENSMUST00000249375 Warning: No sequence found for ENSMUST00000249330 Updated longest transcript for ENSMUSG00000095909: 1303 Updated longest transcript for ENSMUSG00000073650: 1753 Warning: No sequence found for ENSMUST00000249328 Updated longest transcript for ENSMUSG00000099216: 646 Warning: No sequence found for ENSMUST00000249248 Updated longest transcript for ENSMUSG00000121575: 107 Warning: No sequence found for ENSMUST00000249601 Warning: No sequence found for ENSMUST00000249678 Warning: No sequence found for ENSMUST00000249566 Updated longest transcript for ENSMUSG00000063235: 2845 Warning: No sequence found for ENSMUST00000249357 Warning: No sequence found for ENSMUST00000249605 Warning: No sequence found for ENSMUST00000249878 Updated longest transcript for ENSMUSG00000091601: 1341 Updated longest transcript for ENSMUSG00000090093: 1792 Warning: No sequence found for ENSMUST00000249591 Warning: No sequence found for ENSMUST00000249830 Warning: No sequence found for ENSMUST00000249609 Updated longest transcript for ENSMUSG00000015869: 1096 Warning: No sequence found for ENSMUST00000249540 Updated longest transcript for ENSMUSG00000094157: 1820 Warning: No sequence found for ENSMUST00000249367 Warning: No sequence found for ENSMUST00000249301 Warning: No sequence found for ENSMUST00000249718 Updated longest transcript for ENSMUSG00000079697: 178 Updated longest transcript for ENSMUSG00000051146: 958 Warning: No sequence found for ENSMUST00000249896 Warning: No sequence found for ENSMUST00000249456 Warning: No sequence found for ENSMUST00000240712 Warning: No sequence found for ENSMUST00000249275 Warning: No sequence found for ENSMUST00000249667 Warning: No sequence found for ENSMUST00000249326 Updated longest transcript for ENSMUSG00000092335: 3208 Warning: No sequence found for ENSMUST00000249760 Updated longest transcript for ENSMUSG00000121904: 145 Warning: No sequence found for ENSMUST00000249268 Updated longest transcript for ENSMUSG00000036073: 645 Warning: No sequence found for ENSMUST00000249383 Warning: No sequence found for ENSMUST00000249888 Warning: No sequence found for ENSMUST00000249884 Updated longest transcript for ENSMUSG00000034354: 2166 Updated longest transcript for ENSMUSG00000095029: 154 Warning: No sequence found for ENSMUST00000249314 Warning: No sequence found for ENSMUST00000249664 Updated longest transcript for ENSMUSG00000121605: 121 Updated longest transcript for ENSMUSG00000113201: 931 Warning: No sequence found for ENSMUST00000249744 Updated longest transcript for ENSMUSG00000072258: 848 Warning: No sequence found for ENSMUST00000249893 Updated longest transcript for ENSMUSG00000095648: 1040 Warning: No sequence found for ENSMUST00000249309 Warning: No sequence found for ENSMUST00000249829 Warning: No sequence found for ENSMUST00000249745 Warning: No sequence found for ENSMUST00000249500 Warning: No sequence found for ENSMUST00000249920 Warning: No sequence found for ENSMUST00000249359 Updated longest transcript for ENSMUSG00000078865: 250 Warning: No sequence found for ENSMUST00000249596 Warning: No sequence found for ENSMUST00000249252 Updated longest transcript for ENSMUSG00000121579: 147 Warning: No sequence found for ENSMUST00000249736 Warning: No sequence found for ENSMUST00000249690 Warning: No sequence found for ENSMUST00000249877 Updated longest transcript for ENSMUSG00000023809: 2986 Updated longest transcript for ENSMUSG00000078875: 1037 Warning: No sequence found for ENSMUST00000247848 Warning: No sequence found for ENSMUST00000249630 Warning: No sequence found for ENSMUST00000239530 Warning: No sequence found for ENSMUST00000249550 Warning: No sequence found for ENSMUST00000249614 Warning: No sequence found for ENSMUST00000240550 Warning: No sequence found for ENSMUST00000249883 Warning: No sequence found for ENSMUST00000249264 Updated longest transcript for ENSMUSG00000085549: 356 Warning: No sequence found for ENSMUST00000081291 Updated longest transcript for ENSMUSG00000121583: 2788 Warning: No sequence found for ENSMUST00000249419 Updated longest transcript for ENSMUSG00000078870: 1070 Updated longest transcript for ENSMUSG00000031158: 3353 Warning: No sequence found for ENSMUST00000249836 Warning: No sequence found for ENSMUST00000249373 Updated longest transcript for ENSMUSG00000095717: 935 Warning: No sequence found for ENSMUST00000239512 Warning: No sequence found for ENSMUST00000249616 Warning: No sequence found for ENSMUST00000249077 Updated longest transcript for ENSMUSG00000036779: 2452 Warning: No sequence found for ENSMUST00000249707 Warning: No sequence found for ENSMUST00000249873 Updated longest transcript for ENSMUSG00000046805: 1943 Warning: No sequence found for ENSMUST00000249688 Warning: No sequence found for ENSMUST00020183811 Updated longest transcript for ENSMUSG00000027379: 127 Warning: No sequence found for ENSMUST00000249916 Updated longest transcript for ENSMUSG00000079525: 204 Warning: No sequence found for ENSMUST00000249492 Warning: No sequence found for ENSMUST00000239553 Warning: No sequence found for ENSMUST00000249548 Updated longest transcript for ENSMUSG00000095318: 1251 Warning: No sequence found for ENSMUST00000249874 Warning: No sequence found for ENSMUST00000249087 Warning: No sequence found for ENSMUST00000249360 Warning: No sequence found for ENSMUST00000249417 Warning: No sequence found for ENSMUST00000249261 Updated longest transcript for ENSMUSG00000073448: 815 Warning: No sequence found for ENSMUST00000249746 Updated longest transcript for ENSMUSG00000033200: 550 Warning: No sequence found for ENSMUST00000249686 Updated longest transcript for ENSMUSG00000006276: 1559 Completed parsing ARE_score_data/3primeUTR_AREscore_GRC39_full.txt. Total genes: 21540 Completed parsing ARE score data and calculating motif counts. Updating 6 `upregulated` files in upregulated_genes... Processed 1/6 files. Current file: BM_upregulated_annotated.csv Processed 2/6 files. Current file: Spleen_upregulated_annotated.csv Processed 3/6 files. Current file: Ovary_upregulated_annotated.csv Processed 4/6 files. Current file: Lung_upregulated_annotated.csv Processed 5/6 files. Current file: Liver_upregulated_annotated.csv Processed 6/6 files. Current file: Kidney_upregulated_annotated.csv Completed updating files in upregulated_genes. Updating 6 `unregulated` files in unregulated_genes... Processed 1/6 files. Current file: Kidney_unregulated_annotated.csv Processed 2/6 files. Current file: Spleen_unregulated_annotated.csv Processed 3/6 files. Current file: Lung_unregulated_annotated.csv Processed 4/6 files. Current file: BM_unregulated_annotated.csv Processed 5/6 files. Current file: Ovary_unregulated_annotated.csv Processed 6/6 files. Current file: Liver_unregulated_annotated.csv Completed updating files in unregulated_genes. Updating 6 `downregulated` files in downregulated_genes... Processed 1/6 files. Current file: Spleen_downregulated_annotated.csv Processed 2/6 files. Current file: BM_downregulated_annotated.csv Processed 3/6 files. Current file: Ovary_downregulated_annotated.csv Processed 4/6 files. Current file: Lung_downregulated_annotated.csv Processed 5/6 files. Current file: Kidney_downregulated_annotated.csv Processed 6/6 files. Current file: Liver_downregulated_annotated.csv Completed updating files in downregulated_genes. All files have been updated successfully.
Below will be my first iteration of a upset plot wiht two panes includng ARE score information and overlapping set information
In [4]:
import os
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from upsetplot import UpSet, from_indicators
# Function to create the combined UpSet and Violin plot
def create_upset_violin(data, title, output_file, color):
indicator_cols = ['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']
# Ensure all indicator columns are boolean type
data[indicator_cols] = data[indicator_cols].astype(bool)
# Filter out rows where ARE_score is NaN
data = data.dropna(subset=['ARE_score'])
data['ARE_score'] = pd.to_numeric(data['ARE_score'], errors='coerce')
# Create the UpSet plot data
upset_data = from_indicators(indicator_cols, data)
# Initialize UpSet plot
upset = UpSet(upset_data, subset_size='count', sort_by='degree', facecolor=color)
# Add the violin plots for ARE_score values
upset.add_catplot(
value='ARE_score',
kind='violin',
elements=2,
color=color
)
# Plot UpSet with catplots
upset.plot()
plt.suptitle(f'{title} Genes with ARE Score Distribution')
# Save the combined plot
plt.savefig(output_file)
plt.show()
# Load and concatenate data from all updated files in the specified directory
def load_and_concat_data(directory):
files = [os.path.join(directory, file) for file in os.listdir(directory) if file.endswith('_annotated_updated.csv')]
data = pd.concat((pd.read_csv(file) for file in files), ignore_index=True)
return data
# Load upregulated and downregulated data
up_data = load_and_concat_data("upregulated_genes_updated")
down_data = load_and_concat_data("downregulated_genes_updated")
# Create UpSet plots with violin plots
create_upset_violin(up_data, 'Upregulated', 'upregulated_genes_upset_violin.png', 'red')
create_upset_violin(down_data, 'Downregulated', 'downregulated_genes_upset_violin.png', 'blue')
/tmp/ipykernel_2823251/4268302770.py:16: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
data['ARE_score'] = pd.to_numeric(data['ARE_score'], errors='coerce')
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
/tmp/ipykernel_2823251/4268302770.py:16: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
data['ARE_score'] = pd.to_numeric(data['ARE_score'], errors='coerce')
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
In [5]:
import os
import pandas as pd
# Helper function to load and concatenate data from all updated files in the specified directory
def load_and_concat_data(directory):
files = [os.path.join(directory, file) for file in os.listdir(directory) if file.endswith('_annotated_updated.csv')]
data = pd.concat((pd.read_csv(file) for file in files), ignore_index=True)
return data
# Load upregulated data
upregulated_path = "upregulated_genes_updated"
up_data = load_and_concat_data(upregulated_path)
# Ensure all indicator columns are boolean type
indicator_cols = ['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']
up_data[indicator_cols] = up_data[indicator_cols].astype(bool)
# Filter to only include protein_coding genes
up_data = up_data[up_data['gene_biotype'] == 'protein_coding']
# Calculate the sum of binary indicators for each gene
up_data['sum_tissues'] = up_data[indicator_cols].sum(axis=1)
# List genes that are upregulated in 5 or more tissues
upregulated_5_or_more = up_data[up_data['sum_tissues'] >= 5]
# Display the list of gene IDs
upregulated_5_or_more_ids = upregulated_5_or_more['ID']
print(upregulated_5_or_more_ids.tolist())
['ENSMUSG00000035352', 'ENSMUSG00000041481', 'ENSMUSG00000091694', 'ENSMUSG00000035352', 'ENSMUSG00000041481', 'ENSMUSG00000091694', 'ENSMUSG00000091694', 'ENSMUSG00000041481', 'ENSMUSG00000035352', 'ENSMUSG00000091694', 'ENSMUSG00000035352', 'ENSMUSG00000041481', 'ENSMUSG00000091694', 'ENSMUSG00000035352', 'ENSMUSG00000041481', 'ENSMUSG00000091694']
Now I make the same plot again but this time I added strip plots instead of box and whisker plots
In [6]:
import os
import pandas as pd
import matplotlib.pyplot as plt
from upsetplot import UpSet, from_indicators
# Function to create the combined UpSet and Violin plot
def create_upset_violin(data, title, output_file, color):
indicator_cols = ['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']
# Ensure all indicator columns are boolean type
data[indicator_cols] = data[indicator_cols].astype(bool)
# Filter out rows where ARE_score is NaN and Gene.biotype is not 'protein_coding'
data = data[data['gene_biotype'] == 'protein_coding']
data = data.dropna(subset=['ARE_score'])
data['ARE_score'] = pd.to_numeric(data['ARE_score'], errors='coerce')
# Group by gene ID and aggregate the Boolean indicators
data_agg = data.groupby('ID')[indicator_cols + ['ARE_score']].max()
# Ensure every row is treated as unique for UpSet plot
data_agg = data_agg.reset_index()
# Create the UpSet plot data
upset_data = from_indicators(indicator_cols, data_agg)
# Initialize UpSet plot with desired formatting
upset = UpSet(
upset_data,
subset_size='count',
sort_by='-degree',
facecolor=color,
show_counts=True,
max_subset_size=30 # Limiting the large number of insignificant intersections
)
# Add the violin plots for ARE_score values
upset.add_catplot(
value='ARE_score',
kind='strip',
elements=2,
color=color
)
# Plot UpSet with catplots
upset.plot()
# Add title for the combined plot
plt.suptitle(f'{title} Genes with ARE Score Distribution')
# Save the combined plot
plt.savefig(output_file)
plt.show()
# Load and concatenate data from all updated files in the specified directory
def load_and_concat_data(directory):
files = [os.path.join(directory, file) for file in os.listdir(directory) if file.endswith('_annotated_updated.csv')]
data = pd.concat((pd.read_csv(file) for file in files), ignore_index=True)
return data
# Load upregulated and downregulated data
up_data = load_and_concat_data("upregulated_genes_updated")
down_data = load_and_concat_data("downregulated_genes_updated")
# Create UpSet plots with violin plots
create_upset_violin(up_data, 'Upregulated', 'upregulated_genes_upset_violin.png', 'red')
create_upset_violin(down_data, 'Downregulated', 'downregulated_genes_upset_violin.png', 'blue')
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
Now I use boxem plots and include a line of best fit for the medians
In [7]:
import os
import pandas as pd
import matplotlib.pyplot as plt
from upsetplot import UpSet, from_indicators
from sklearn.linear_model import LinearRegression
import numpy as np
# Function to create the combined UpSet and Violin plot
def create_upset_violin(data, title, output_file, color):
indicator_cols = ['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']
# Ensure all indicator columns are boolean type
data[indicator_cols] = data[indicator_cols].astype(bool)
# Filter out rows where ARE_score is NaN and Gene.biotype is not 'protein_coding'
data = data[data['gene_biotype'] == 'protein_coding']
data = data.dropna(subset=['ARE_score'])
data['ARE_score'] = pd.to_numeric(data['ARE_score'], errors='coerce')
# Group by gene ID and aggregate the Boolean indicators
data_agg = data.groupby('ID')[indicator_cols + ['ARE_score']].max()
# Ensure every row is treated as unique for UpSet plot
data_agg = data_agg.reset_index()
# Create the UpSet plot data
upset_data = from_indicators(indicator_cols, data_agg)
# Initialize UpSet plot with desired formatting
upset = UpSet(
upset_data,
subset_size='count',
sort_by='-degree',
facecolor=color,
show_counts=True,
# max_subset_size=30 # Limiting the large number of insignificant intersections
)
# Add the violin plots for ARE_score values
upset.add_catplot(
value='ARE_score',
kind='boxen',
elements=10,
color=color
)
# Plot UpSet with catplots
fig = plt.figure(figsize=(10, 6))
upset.plot(fig=fig)
# Add title for the combined plot
plt.suptitle(f'{title} Genes with ARE Score Distribution')
# Calculate mean ARE_score for each subset
subset_means = upset_data.groupby(upset_data.index)['ARE_score'].mean().reset_index()
subset_means.columns = ['Subset', 'Mean_ARE_score']
# Prepare data for linear regression
X = np.arange(len(subset_means)).reshape(-1, 1)
y = subset_means['Mean_ARE_score'].values
# Perform linear regression
model = LinearRegression()
model.fit(X, y)
y_pred = model.predict(X)
# Plot the line of best fit
plt.plot(X, y_pred, color='blue', lw=2, linestyle='--')
# Save the combined plot
plt.savefig(output_file)
plt.show()
# Load and concatenate data from all updated files in the specified directory
def load_and_concat_data(directory):
files = [os.path.join(directory, file) for file in os.listdir(directory) if file.endswith('_annotated_updated.csv')]
data = pd.concat((pd.read_csv(file) for file in files), ignore_index=True)
return data
# Load upregulated and downregulated data
up_data = load_and_concat_data("upregulated_genes_updated")
down_data = load_and_concat_data("downregulated_genes_updated")
# Create UpSet plots with violin plots and lines of best fit
create_upset_violin(up_data, 'Upregulated', 'upregulated_genes_upset_violin.png', 'red')
create_upset_violin(down_data, 'Downregulated', 'downregulated_genes_upset_violin.png', 'blue')
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
Same thing just using polynomial line of best fit
In [8]:
import os
import pandas as pd
import matplotlib.pyplot as plt
from upsetplot import UpSet, from_indicators
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
import numpy as np
# Function to create the combined UpSet and Violin plot
def create_upset_violin(data, title, output_file, color):
indicator_cols = ['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']
# Ensure all indicator columns are boolean type
data[indicator_cols] = data[indicator_cols].astype(bool)
# Filter out rows where ARE_score is NaN and Gene.biotype is not 'protein_coding'
data = data[data['gene_biotype'] == 'protein_coding']
data = data.dropna(subset=['ARE_score'])
data['ARE_score'] = pd.to_numeric(data['ARE_score'], errors='coerce')
# Group by gene ID and aggregate the Boolean indicators
data_agg = data.groupby('ID')[indicator_cols + ['ARE_score']].max()
# Ensure every row is treated as unique for UpSet plot
data_agg = data_agg.reset_index()
# Create the UpSet plot data
upset_data = from_indicators(indicator_cols, data_agg)
# Initialize UpSet plot with desired formatting
upset = UpSet(
upset_data,
subset_size='count',
sort_by='-degree',
facecolor=color,
show_counts=True,
# max_subset_size=30 # Limiting the large number of insignificant intersections
)
# Add the violin plots for ARE_score values
upset.add_catplot(
value='ARE_score',
kind='boxen',
elements=10,
color=color
)
# Plot UpSet with catplots
fig = plt.figure(figsize=(10, 6))
upset.plot(fig=fig)
# Add title for the combined plot
plt.suptitle(f'{title} Genes with ARE Score Distribution')
# Calculate mean ARE_score for each subset
subset_means = upset_data.groupby(upset_data.index)['ARE_score'].mean().reset_index()
subset_means.columns = ['Subset', 'Mean_ARE_score']
# Prepare data for polynomial regression
X = np.arange(len(subset_means)).reshape(-1, 1)
y = subset_means['Mean_ARE_score'].values
# Perform polynomial regression (degree 2 or 3 for better accuracy)
polynomial_features = PolynomialFeatures(degree=2)
X_poly = polynomial_features.fit_transform(X)
model = LinearRegression()
model.fit(X_poly, y)
y_poly_pred = model.predict(X_poly)
# Plot the curve of best fit
plt.plot(X, y_poly_pred, color='blue', lw=2, linestyle='--')
# Save the combined plot
plt.savefig(output_file)
plt.show()
# Load and concatenate data from all updated files in the specified directory
def load_and_concat_data(directory):
files = [os.path.join(directory, file) for file in os.listdir(directory) if file.endswith('_annotated_updated.csv')]
data = pd.concat((pd.read_csv(file) for file in files), ignore_index=True)
return data
# Load upregulated and downregulated data
up_data = load_and_concat_data("upregulated_genes_updated")
down_data = load_and_concat_data("downregulated_genes_updated")
# Create UpSet plots with violin plots and polynomial lines of best fit
create_upset_violin(up_data, 'Upregulated', 'upregulated_genes_upset_violin.png', 'red')
create_upset_violin(down_data, 'Downregulated', 'downregulated_genes_upset_violin.png', 'blue')
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:795: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linewidth"].fillna(1, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:796: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["facecolor"].fillna(self._facecolor, inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:797: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["edgecolor"].fillna(styles["facecolor"], inplace=True)
/home/gsgeorge/.local/lib/python3.12/site-packages/upsetplot/plotting.py:798: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.
For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.
styles["linestyle"].fillna("solid", inplace=True)
In [9]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import glob
# Function to read all CSV files from updated folders
def read_data(regulation_type):
path = f'{regulation_type}_genes_updated/*.csv'
filenames = glob.glob(path)
dfs = []
for filename in filenames:
df = pd.read_csv(filename)
tissue = filename.split('_')[0].split('/')[-1] # Extract tissue info from the file name
df['Tissue'] = tissue
df['Regulation'] = regulation_type.capitalize()
dfs.append(df)
return pd.concat(dfs, ignore_index=True)
# Reading the data
upregulated_data = read_data("upregulated")
downregulated_data = read_data("downregulated")
unregulated_data = read_data("unregulated")
# Combine all data
combined_data = pd.concat([upregulated_data, downregulated_data, unregulated_data], ignore_index=True)
# Function to calculate the number of intersections and mean ARE score
def prepare_data(df):
df['NumTissues'] = df[['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']].sum(axis=1)
return df[['NumTissues', 'ARE_score', 'Regulation']]
# Preparing data for plotting
prepared_data = prepare_data(combined_data)
# Plotting function
def plot_jitter_violins(data, regulation_type, color):
plt.figure(figsize=(10, 6))
sns.violinplot(x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type.capitalize()],
inner=None, color=color, scale='width', alpha=0.6)
sns.stripplot(x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type.capitalize()],
jitter=True, color=color, size=2.5)
plt.xlabel("Number of Tissues")
plt.ylabel("ARE Score")
plt.title(f"ARE Score Distribution for {regulation_type.capitalize()} Genes")
plt.grid(True)
plt.show()
# Plotting the scores
plot_jitter_violins(prepared_data, "upregulated", "orange")
plot_jitter_violins(prepared_data, "downregulated", "blue")
plot_jitter_violins(prepared_data, "unregulated", "black")
/tmp/ipykernel_2823251/3320125982.py:40: FutureWarning: The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect. sns.violinplot(x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type.capitalize()],
/tmp/ipykernel_2823251/3320125982.py:40: FutureWarning: The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect. sns.violinplot(x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type.capitalize()],
/tmp/ipykernel_2823251/3320125982.py:40: FutureWarning: The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect. sns.violinplot(x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type.capitalize()],
In [10]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import glob
# Function to read all CSV files from updated folders
def read_data(regulation_type):
path = f'{regulation_type}_genes_updated/*.csv'
filenames = glob.glob(path)
dfs = []
for filename in filenames:
df = pd.read_csv(filename)
tissue = filename.split('_')[0].split('/')[-1] # Extract tissue info from the file name
df['Tissue'] = tissue
df['Regulation'] = regulation_type.capitalize()
dfs.append(df)
return pd.concat(dfs, ignore_index=True)
# Reading the data
upregulated_data = read_data("upregulated")
downregulated_data = read_data("downregulated")
unregulated_data = read_data("unregulated")
# Combine all data
combined_data = pd.concat([upregulated_data, downregulated_data, unregulated_data], ignore_index=True)
# Function to calculate the number of intersections and prepare data for plotting
def prepare_data(df):
df['NumTissues'] = df[['BM', 'Spleen', 'Ovary', 'Liver', 'Lung', 'Kidney']].sum(axis=1)
return df[['NumTissues', 'ARE_score', 'Regulation']]
# Preparing data for plotting
prepared_data = prepare_data(combined_data)
# Plotting function
def plot_jitter_violins(ax, data, regulation_type, color):
sns.violinplot(ax=ax, x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type],
inner=None, color=color, scale='width', alpha=0.6)
sns.stripplot(ax=ax, x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type],
jitter=True, color=color, size=2.5)
ax.set_title(f"{regulation_type} Genes", fontsize=14, weight='bold')
ax.set_xlabel("Number of Tissues", fontsize=12)
ax.set_ylabel("ARE Score", fontsize=12)
# Creating the figure and subplots
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 18), sharex=True)
fig.suptitle('ARE Score Distribution Across Number of Tissues', fontsize=16, weight='bold')
# Plotting the scores for each regulation type
plot_jitter_violins(axes[0], prepared_data, "Upregulated", "orange")
plot_jitter_violins(axes[1], prepared_data, "Downregulated", "blue")
plot_jitter_violins(axes[2], prepared_data, "Unregulated", "black")
# Adjust layout
plt.tight_layout(rect=[0, 0, 1, 0.95]) # Adjust subplots to fit title
plt.subplots_adjust(hspace=0.4)
# Adding legends to individual plots
axes[0].legend(['Upregulated'], loc='upper right')
axes[1].legend(['Downregulated'], loc='upper right')
axes[2].legend(['Unregulated'], loc='upper right')
plt.show()
/tmp/ipykernel_2823251/2060466322.py:39: FutureWarning: The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect. sns.violinplot(ax=ax, x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type], /tmp/ipykernel_2823251/2060466322.py:39: FutureWarning: The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect. sns.violinplot(ax=ax, x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type], /tmp/ipykernel_2823251/2060466322.py:39: FutureWarning: The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect. sns.violinplot(ax=ax, x='NumTissues', y='ARE_score', data=data[data['Regulation'] == regulation_type],
Corrected tissue specific and overall violin plots
In [11]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ks_2samp
from statsmodels.stats.multitest import multipletests
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Conducting a KS-test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(up_scores, down_scores)
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 0.5, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
# Plot for overall ARE score violin plot comparing up and downregulated genes across all tissues
sns.violinplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', scale='area',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
sns.stripplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Regulation Types"])
# Add means annotation for overall plot
overall_up_mean = up_scores.mean()
overall_down_mean = down_scores.mean()
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
max_score = max(combined_data['ARE_score'])
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max_score, p_val=p_value, h=max_score * 0.1)
# Calculate and store p-values for each tissue
p_values = []
for tissue, group in combined_data.groupby('Tissue'):
up_scores_tissue = group[group['Regulation'] == 'Upregulated']['ARE_score']
down_scores_tissue = group[group['Regulation'] == 'Downregulated']['ARE_score']
if not up_scores_tissue.empty and not down_scores_tissue.empty:
ks_stat, p_value = ks_2samp(up_scores_tissue, down_scores_tissue)
p_values.append(p_value)
else:
p_values.append(1) # Assign a non-significant p-value for cases with empty groups
# Apply multiple testing correction (e.g., Benjamini-Hochberg)
reject, corrected_p_vals, _, _ = multipletests(p_values, method='fdr_bh')
# Plot for ARE score distributions for each tissue, now ordered
violinplot = sns.violinplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', scale='width',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
sns.stripplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_title('ARE Scores by Tissue and Regulation Type')
axes[1].set_ylabel('ARE Score')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', bbox_to_anchor=(1, 1), title='Regulation')
# Get positions for corrected p-values
positions = axes[1].get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
# Plot corrected p-values on the second subplot
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
if not group[group['Regulation'] == 'Upregulated']['ARE_score'].empty and not group[group['Regulation'] == 'Downregulated']['ARE_score'].empty:
up_pos = position_map[tissue] - 0.2 # Adjust depending on position
down_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['ARE_score'])
add_p_val_annotation(axes[1], up_pos, down_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("Overall_ARE_distribution_including_zeros.svg", format="svg")
plt.show()
Series([], Name: Regulation, dtype: int64)
/tmp/ipykernel_2823251/2819809908.py:77: FutureWarning:
The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='area'` for the same effect.
sns.violinplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', scale='area',
/tmp/ipykernel_2823251/2819809908.py:84: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
axes[0].set_xticklabels(["All Regulation Types"])
/tmp/ipykernel_2823251/2819809908.py:98: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for tissue, group in combined_data.groupby('Tissue'):
/tmp/ipykernel_2823251/2819809908.py:111: FutureWarning:
The `scale` parameter has been renamed and will be removed in v0.15.0. Pass `density_norm='width'` for the same effect.
violinplot = sns.violinplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', scale='width',
/tmp/ipykernel_2823251/2819809908.py:129: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
In [12]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ks_2samp
from statsmodels.stats.multitest import multipletests
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
print(min(combined_data))
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Conducting a KS-test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(up_scores, down_scores)
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 0.5, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
# Plot for overall ARE score box plot comparing up and downregulated genes across all tissues
sns.boxplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0], showfliers=False)
sns.stripplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6, palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Regulation Types"])
# Add means annotation for overall plot
overall_up_mean = up_scores.mean()
overall_down_mean = down_scores.mean()
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
max_score = max(combined_data['ARE_score'])
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max_score, p_val=p_value, h=max_score * 0.1)
# Calculate and store p-values for each tissue
p_values = []
for tissue, group in combined_data.groupby('Tissue'):
up_scores_tissue = group[group['Regulation'] == 'Upregulated']['ARE_score']
down_scores_tissue = group[group['Regulation'] == 'Downregulated']['ARE_score']
if not up_scores_tissue.empty and not down_scores_tissue.empty:
ks_stat, p_value = ks_2samp(up_scores_tissue, down_scores_tissue)
p_values.append(p_value)
else:
p_values.append(1) # Assign a non-significant p-value for cases with empty groups
# Apply multiple testing correction (e.g., Benjamini-Hochberg)
reject, corrected_p_vals, _, _ = multipletests(p_values, method='fdr_bh')
# Plot for ARE score distributions for each tissue, now ordered
sns.boxplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1], showfliers=False)
sns.stripplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6, palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_title('ARE Scores by Tissue and Regulation Type')
axes[1].set_ylabel('ARE Score')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', bbox_to_anchor=(1, 1), title='Regulation')
# Get positions for corrected p-values
positions = axes[1].get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
# Plot corrected p-values on the second subplot
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
if not group[group['Regulation'] == 'Upregulated']['ARE_score'].empty and not group[group['Regulation'] == 'Downregulated']['ARE_score'].empty:
up_pos = position_map[tissue] - 0.2 # Adjust depending on position
down_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['ARE_score'])
add_p_val_annotation(axes[1], up_pos, down_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("Overall_ARE_distribution_including_zeros.svg", format="svg")
plt.show()
5mer_count Series([], Name: Regulation, dtype: int64)
/tmp/ipykernel_2823251/3324192236.py:82: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
axes[0].set_xticklabels(["All Regulation Types"])
/tmp/ipykernel_2823251/3324192236.py:96: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for tissue, group in combined_data.groupby('Tissue'):
/tmp/ipykernel_2823251/3324192236.py:125: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
Same plot as above just with a sqrt() transformed y-axis
In [13]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ks_2samp
from statsmodels.stats.multitest import multipletests
import numpy as np
from matplotlib.ticker import ScalarFormatter
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Conducting a KS-test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(up_scores, down_scores)
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 0.5, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Apply square root transformation
combined_data['ARE_score_transformed'] = np.sqrt(combined_data['ARE_score'])
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score_transformed'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
# Plot for overall ARE score box plot comparing up and downregulated genes across all tissues
sns.boxplot(data=combined_data, x='dummy', y='ARE_score_transformed', hue='Regulation',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0], showfliers=False)
sns.stripplot(data=combined_data, x='dummy', y='ARE_score_transformed', hue='Regulation',
dodge=True, jitter=True, alpha=0.6, palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score (Square Root Transformed)')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Regulation Types"])
# Add means annotation for overall plot
overall_up_mean = np.sqrt(up_scores).mean()
overall_down_mean = np.sqrt(down_scores).mean()
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
max_score = max(combined_data['ARE_score_transformed'])
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max_score, p_val=p_value, h=max_score * 0.1)
# Calculate and store p-values for each tissue
p_values = []
for tissue, group in combined_data.groupby('Tissue'):
up_scores_tissue = group[group['Regulation'] == 'Upregulated']['ARE_score_transformed']
down_scores_tissue = group[group['Regulation'] == 'Downregulated']['ARE_score_transformed']
if not up_scores_tissue.empty and not down_scores_tissue.empty:
ks_stat, p_value = ks_2samp(up_scores_tissue, down_scores_tissue)
p_values.append(p_value)
else:
p_values.append(1) # Assign a non-significant p-value for cases with empty groups
# Apply multiple testing correction (e.g., Benjamini-Hochberg)
reject, corrected_p_vals, _, _ = multipletests(p_values, method='fdr_bh')
# Plot for ARE score distributions for each tissue, now ordered
sns.boxplot(data=combined_data, x='Tissue', y='ARE_score_transformed', hue='Regulation',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1], showfliers=False)
sns.stripplot(data=combined_data, x='Tissue', y='ARE_score_transformed', hue='Regulation',
dodge=True, jitter=True, alpha=0.6, palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_title('ARE Scores by Tissue and Regulation Type')
axes[1].set_ylabel('ARE Score (Square Root Transformed)')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', bbox_to_anchor=(1, 1), title='Regulation')
# Get positions for corrected p-values
positions = axes[1].get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
# Plot corrected p-values on the second subplot
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
if not group[group['Regulation'] == 'Upregulated']['ARE_score_transformed'].empty and not group[group['Regulation'] == 'Downregulated']['ARE_score_transformed'].empty:
up_pos = position_map[tissue] - 0.2 # Adjust depending on position
down_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['ARE_score_transformed'])
add_p_val_annotation(axes[1], up_pos, down_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("Overall_ARE_distribution_square_root_transformed.svg", format="svg")
plt.show()
Series([], Name: Regulation, dtype: int64)
/tmp/ipykernel_2823251/777753550.py:89: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
axes[0].set_xticklabels(["All Regulation Types"])
/tmp/ipykernel_2823251/777753550.py:103: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for tissue, group in combined_data.groupby('Tissue'):
/tmp/ipykernel_2823251/777753550.py:134: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
Sqrt transformed:
In [3]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import ks_2samp
from statsmodels.stats.multitest import multipletests
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Conducting a KS-test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(up_scores, down_scores)
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 0.5, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
# Apply square root transformation to ARE scores
combined_data['sqrt_ARE_score'] = np.sqrt(combined_data['ARE_score'])
# Plot for overall ARE score box plot comparing up and downregulated genes across all tissues
sns.boxplot(data=combined_data, x='dummy', y='sqrt_ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0], showfliers=False)
sns.stripplot(data=combined_data, x='dummy', y='sqrt_ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6, palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score (sqrt-transformed)')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Regulation Types"])
# Add means annotation for overall plot
overall_up_mean = np.sqrt(overall_up_mean)
overall_down_mean = np.sqrt(overall_down_mean)
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
max_score = np.sqrt(max(combined_data['ARE_score']))
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max_score, p_val=p_value, h=max_score * 0.1)
# Calculate and store p-values for each tissue
p_values = []
for tissue, group in combined_data.groupby('Tissue'):
up_scores_tissue = np.sqrt(group[group['Regulation'] == 'Upregulated']['ARE_score'])
down_scores_tissue = np.sqrt(group[group['Regulation'] == 'Downregulated']['ARE_score'])
if not up_scores_tissue.empty and not down_scores_tissue.empty:
ks_stat, p_value = ks_2samp(up_scores_tissue, down_scores_tissue)
p_values.append(p_value)
else:
p_values.append(1) # Assign a non-significant p-value for cases with empty groups
# Apply multiple testing correction (e.g., Benjamini-Hochberg)
reject, corrected_p_vals, _, _ = multipletests(p_values, method='fdr_bh')
# Plot for ARE score distributions for each tissue, now ordered
sns.boxplot(data=combined_data, x='Tissue', y='sqrt_ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1], showfliers=False)
sns.stripplot(data=combined_data, x='Tissue', y='sqrt_ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6, palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_title('ARE Scores by Tissue and Regulation Type')
axes[1].set_ylabel('ARE Score (sqrt-transformed)')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', bbox_to_anchor=(1, 1), title='Regulation')
# Get positions for corrected p-values
positions = axes[1].get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
# Plot corrected p-values on the second subplot
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
if not group[group['Regulation'] == 'Upregulated']['ARE_score'].empty and not group[group['Regulation'] == 'Downregulated']['ARE_score'].empty:
up_pos = position_map[tissue] - 0.2 # Adjust depending on position
down_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['sqrt_ARE_score'])
add_p_val_annotation(axes[1], up_pos, down_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("/mnt/work_1/gsgeorge/mus_musculus/TPM_analysis/Overall_ARE_distribution_including_zeros.svg", format="svg")
plt.show()
Series([], Name: Regulation, dtype: int64)
/tmp/ipykernel_303325/1986591938.py:86: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
axes[0].set_xticklabels(["All Regulation Types"])
/tmp/ipykernel_303325/1986591938.py:100: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for tissue, group in combined_data.groupby('Tissue'):
/tmp/ipykernel_303325/1986591938.py:129: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
for (tissue, group), p_value in zip(combined_data.groupby('Tissue'), corrected_p_vals):
Including 0 in visualization and statistical testing
In [14]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ks_2samp
from statsmodels.stats.multitest import multipletests
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Conducting a KS-test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(up_scores, down_scores)
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 30, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
# Plot for overall ARE score violin plot comparing up and downregulated genes across all tissues
sns.violinplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', split=True,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
sns.stripplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Upregulated Coding 3' UTR Sequences"])
# Add means annotation for overall plot
overall_up_mean = up_scores.mean()
overall_down_mean = down_scores.mean()
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max(combined_data['ARE_score']), p_val=p_value, h=max(combined_data['ARE_score']) * 0.1)
# Calculate and store p-values for each tissue
p_values = []
for tissue, group in combined_data.groupby('Tissue', observed=False):
wt_scores = group[group['Regulation'] == 'Upregulated']['ARE_score']
ko_scores = group[group['Regulation'] == 'Downregulated']['ARE_score']
if not wt_scores.empty and not ko_scores.empty:
ks_stat, p_value = ks_2samp(wt_scores, ko_scores)
p_values.append(p_value)
else:
p_values.append(1) # Assign a non-significant p-value for cases with empty groups
# Apply multiple testing correction (e.g., Benjamini-Hochberg)
reject, corrected_p_vals, _, _ = multipletests(p_values, method='fdr_bh')
# Plot for ARE score distributions for each tissue, now ordered
violinplot = sns.violinplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', split=True,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
sns.stripplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_title('ARE Scores by Tissue and Regulation Type')
axes[1].set_ylabel('ARE Score')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', bbox_to_anchor=(1, 1), title='Regulation')
# Get positions for corrected p-values
positions = violinplot.get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
# Plot corrected p-values on the second subplot
for (tissue, group), p_value in zip(combined_data.groupby('Tissue', observed=False), corrected_p_vals):
if not group[group['Regulation'] == 'Upregulated']['ARE_score'].empty and not group[group['Regulation'] == 'Downregulated']['ARE_score'].empty:
wt_pos = position_map[tissue] - 0.2 # Adjust depending on position
ko_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['ARE_score'])
add_p_val_annotation(axes[1], wt_pos, ko_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("Overall_ARE_distribution_including_zeros.svg", format="svg")
plt.show()
Series([], Name: Regulation, dtype: int64)
/tmp/ipykernel_2823251/2829530817.py:84: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. axes[0].set_xticklabels(["All Upregulated Coding 3' UTR Sequences"])
Using a mann-whit U test here
In [15]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import mannwhitneyu
from statsmodels.stats.multitest import multipletests
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Conducting a Mann-Whitney U test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
u_stat, p_value = mannwhitneyu(up_scores, down_scores, alternative='two-sided')
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 30, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
# Plot for overall ARE score violin plot comparing up and downregulated genes across all tissues
sns.violinplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', split=True,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
sns.stripplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Upregulated Coding 3' UTR Sequences"])
# Add means annotation for overall plot
overall_up_mean = up_scores.mean()
overall_down_mean = down_scores.mean()
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max(combined_data['ARE_score']), p_val=p_value, h=max(combined_data['ARE_score']) * 0.1)
# Calculate and store p-values for each tissue
p_values = []
for tissue, group in combined_data.groupby('Tissue', observed=False):
wt_scores = group[group['Regulation'] == 'Upregulated']['ARE_score']
ko_scores = group[group['Regulation'] == 'Downregulated']['ARE_score']
if not wt_scores.empty and not ko_scores.empty:
u_stat, p_value = mannwhitneyu(wt_scores, ko_scores, alternative='two-sided')
p_values.append(p_value)
else:
p_values.append(1) # Assign a non-significant p-value for cases with empty groups
# Apply multiple testing correction (e.g., Benjamini-Hochberg)
reject, corrected_p_vals, _, _ = multipletests(p_values, method='fdr_bh')
# Plot for ARE score distributions for each tissue, now ordered
violinplot = sns.violinplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', split=True,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
sns.stripplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_title('ARE Scores by Tissue and Regulation Type')
axes[1].set_ylabel('ARE Score')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', bbox_to_anchor=(1, 1), title='Regulation')
# Get positions for corrected p-values
positions = violinplot.get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
# Plot corrected p-values on the second subplot
for (tissue, group), p_value in zip(combined_data.groupby('Tissue', observed=False), corrected_p_vals):
if not group[group['Regulation'] == 'Upregulated']['ARE_score'].empty and not group[group['Regulation'] == 'Downregulated']['ARE_score'].empty:
wt_pos = position_map[tissue] - 0.2 # Adjust depending on position
ko_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['ARE_score'])
add_p_val_annotation(axes[1], wt_pos, ko_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("Overall_ARE_distribution_including_zeros.svg", format="svg")
plt.show()
Series([], Name: Regulation, dtype: int64)
/tmp/ipykernel_2823251/3742372327.py:84: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. axes[0].set_xticklabels(["All Upregulated Coding 3' UTR Sequences"])
In [16]:
# Calculate mean ARE scores for upregulated and downregulated genes for each tissue
mean_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].mean().unstack()
mean_scores['Difference'] = mean_scores['Upregulated'] - mean_scores['Downregulated']
mean_scores = mean_scores.sort_values(by='Difference', ascending=False)
print(mean_scores)
Regulation Downregulated Upregulated Difference Tissue Liver 2.335407 4.030804 1.695397 Kidney 1.720312 3.313000 1.592688 Ovary 1.788636 2.862273 1.073636 BM 3.035843 3.911170 0.875326 Spleen 3.086970 3.665610 0.578640 Lung 3.046296 2.980159 -0.066138
/tmp/ipykernel_2823251/308238176.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. mean_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].mean().unstack()
In [17]:
# Calculate mean ARE scores for upregulated and downregulated genes for each tissue
mean_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
mean_scores['Difference'] = mean_scores['Upregulated'] - mean_scores['Downregulated']
mean_scores = mean_scores.sort_values(by='Difference', ascending=False)
print(mean_scores)
Regulation Downregulated Upregulated Difference Tissue Kidney 0.0 1.3 1.3 BM 1.3 2.3 1.0 Lung 1.3 2.3 1.0 Spleen 1.3 2.0 0.7 Liver 1.0 1.3 0.3 Ovary 1.0 1.3 0.3
/tmp/ipykernel_2823251/3645416548.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning. mean_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
This is where I realized that the medians would allow me to order the box and whisker plots correctly based on their ARE score medians, which denote the white line down the boxes
Up and downregulated overall pie charts to be added to the overall volcano plot in post using adobe illustrator
In [18]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ttest_ind
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Proportion for upregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
up_zero_count = (up_scores == 0).sum()
up_nonzero_count = (up_scores > 0).sum()
# Proportion for downregulated genes
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
down_zero_count = (down_scores == 0).sum()
down_nonzero_count = (down_scores > 0).sum()
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Function to plot pie charts and save them
def plot_and_save_pie_chart(labels, sizes, colors, filename):
fig, ax = plt.subplots()
def autopct_func(pct, allvals):
absolute = int(round(pct/100.*sum(allvals)))
return "{:.1f}%\n({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(sizes, labels=labels, colors=colors, autopct=lambda pct: autopct_func(pct, sizes), startangle=90, textprops=dict(color="black"))
for text in autotexts:
# Move the text higher so it fits better
text.set_position((text.get_position()[0], text.get_position()[1] - 0.2))
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.savefig(filename, format='svg')
plt.show() # Close the figure to prevent it from displaying immediately and to free memory
# Plot and save upregulated pie chart
up_labels = ['ARE Score = 0', 'ARE Score > 0']
up_sizes = [up_zero_count, up_nonzero_count]
up_colors = ['lightcoral', 'orange']
plot_and_save_pie_chart(up_labels, up_sizes, up_colors, 'upregulated_overall_pie.svg')
# Plot and save downregulated pie chart
down_labels = ['ARE Score = 0', 'ARE Score > 0']
down_sizes = [down_zero_count, down_nonzero_count]
down_colors = ['lightblue', 'blue']
plot_and_save_pie_chart(down_labels, down_sizes, down_colors, 'downregulated_overall_pie.svg')
Series([], Name: Regulation, dtype: int64)
In [19]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ttest_ind
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Categorize the ARE scores into different categories
def categorize_scores(scores):
categories = pd.cut(scores, bins=[-1, 0, 2, 5, float('inf')], labels=['ARE Score = 0', '0 < ARE Score ≤ 2', '2 < ARE Score < 5', 'ARE Score ≥ 5'])
return categories.value_counts()
# Proportion for upregulated genesplt.savefig('/mnt/work_1/gsgeorge/mus_musculus/TPM_analysis/TPM_distribution_across_tissues.svg', format='svg')
def autopct_func(pct, allvals):
absolute = int(round(pct / 100. * sum(allvals)))
return "{:.1f}%\n({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(categories.values, labels=categories.index, colors=colors, autopct=lambda pct: autopct_func(pct, categories.values), startangle=90, textprops=dict(color="black"))
for text in autotexts:
# Move the text higher so it fits better
text.set_position((text.get_position()[0], text.get_position()[1] - 0.2))
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.savefig(filename, format='svg')
plt.show() # Close the figure to prevent it from displaying immediately and to free memory
# Plot and save upregulated pie chart
up_colors = ['lightcoral', 'orange', 'gold', 'darkred']
plot_and_save_pie_chart(up_categories, up_colors, 'upregulated_overall_pie.svg')
# Plot and save downregulated pie chart
down_colors = ['lightblue', 'blue', 'darkblue', 'navy']
plot_and_save_pie_chart(down_categories, down_colors, 'downregulated_overall_pie.svg')
Series([], Name: Regulation, dtype: int64)
In [20]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ttest_ind
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Categorize the ARE scores into different categories
def categorize_scores(scores):
categories = pd.cut(scores, bins=[-1, 0, 4, float('inf')], labels=['ARE Score = 0', '0 < ARE Score ≤ 4', 'ARE Score > 4'])
return categories.value_counts()
# Proportion for upregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
up_categories = categorize_scores(up_scores)
# Proportion for downregulated genes
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
down_categories = categorize_scores(down_scores)
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Function to plot pie charts and save them
def plot_and_save_pie_chart(categories, colors, filename, startangle=90, explode=(0, 0, 0.1)):
fig, ax = plt.subplots()
def autopct_func(pct, allvals):
absolute = int(round(pct / 100. * sum(allvals)))
return "{:.1f}%\n({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(
categories.values,
labels=categories.index,
colors=colors,
autopct=lambda pct: autopct_func(pct, categories.values),
startangle=startangle,
textprops=dict(color="black"),
explode=explode,
wedgeprops={"edgecolor": "black", "linewidth": 1}
)
for text in autotexts:
# Move the text higher so it fits better
text.set_position((text.get_position()[0], text.get_position()[1] - 0.2))
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.savefig(filename, format='svg')
plt.show() # Close the figure to prevent it from displaying immediately and to free memory
# Set a common start angle for consistency
start_angle = 90
# Explode the 'ARE Score = 0' slice larger than the other slices
explode_factor = (0.1, 0, 0)
# Plot and save upregulated pie chart with a gradient from light orange to dark orange
up_colors = ['orange', 'darkorange', 'orangered']
plot_and_save_pie_chart(up_categories, up_colors, 'upregulated_overall_pie.svg', startangle=start_angle, explode=explode_factor)
# Plot and save downregulated pie chart with a gradient from light blue to dark blue
down_colors = ['lightblue', 'blue', 'darkblue']
plot_and_save_pie_chart(down_categories, down_colors, 'downregulated_overall_pie.svg', startangle=start_angle, explode=explode_factor)
Series([], Name: Regulation, dtype: int64)
In [21]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ttest_ind
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Categorize the ARE scores into different categories
def categorize_scores(scores):
categories = pd.cut(scores, bins=[-1, 0, 2, 5, float('inf')], labels=['ARE Score = 0', '0 < ARE Score ≤ 2', '2 < ARE Score < 5', 'ARE Score ≥ 5'])
return categories.value_counts()
# Proportion for upregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
up_categories = categorize_scores(up_scores)
# Proportion for downregulated genes
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
down_categories = categorize_scores(down_scores)
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.5)
# Function to plot pie charts and save them
def plot_and_save_pie_chart(categories, colors, filename):
fig, ax = plt.subplots()
def autopct_func(pct, allvals):
absolute = int(round(pct / 100. * sum(allvals)))
return "{:.1f}%\n({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(categories.values, labels=categories.index, colors=colors,
autopct=lambda pct: autopct_func(pct, categories.values), startangle=90)
for text in autotexts:
# Move the text higher so it fits better
text.set_position((text.get_position()[0], text.get_position()[1] - 0.2))
text.set_color('white')
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.savefig(filename, format='svg')
plt.show() # Close the figure to prevent it from displaying immediately and to free memory
# Plot and save upregulated pie chart
up_colors = ['#FFD700', '#FF4500', '#FF8C00', '#FFA500'] # Golden rod to dark orange
plot_and_save_pie_chart(up_categories, up_colors, 'upregulated_overall_pie.svg')
# Plot and save downregulated pie chart
down_colors = ['#ADD8E6', '#00008B', '#4682B4', '#87CEEB'] # Light blue to dark blue
plot_and_save_pie_chart(down_categories, down_colors, 'downregulated_overall_pie.svg')
Series([], Name: Regulation, dtype: int64)
Now I am going to exclude kidney and lung and see what happens:
In [22]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ks_2samp
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_annotated_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and filter for valid ARE scores
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Filtering out Lung and Kidney tissues
combined_data = combined_data[~combined_data['Tissue'].isin(['Lung', 'Kidney'])]
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Identify the lowest ARE score for upregulated and downregulated sets
lowest_up_score = combined_data[(combined_data['Regulation'] == 'Upregulated') & (combined_data['ARE_score'] > 0)]['ARE_score'].min()
lowest_down_score = combined_data[(combined_data['Regulation'] == 'Downregulated') & (combined_data['ARE_score'] > 0)]['ARE_score'].min()
print(f"Lowest ARE score in upregulated set: {lowest_up_score}")
print(f"Lowest ARE score in downregulated set: {lowest_down_score}")
# Ensure there are no zero scores involved in statistical calculation
assert lowest_up_score > 0, "There are zero scores in the upregulated set."
assert lowest_down_score > 0, "There are zero scores in the downregulated set."
# Conducting a KS-test between the scores of upregulated and downregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(up_scores, down_scores)
# Function to add p-value annotations
def add_p_val_annotation(ax, x1, x2, y, p_val, h=0.02):
p_text = f'p = {p_val:.2e}'
ax.plot([x1, x1, x2, x2], [y, y + h, y + h, y], color='k')
ax.text((x1 + x2) * .5, y + h, p_text, ha='center', va='bottom', color='k')
# Function to add mean annotations
def add_mean_annotation(ax, x_pos, mean, color):
mean_text = f'mean = {mean:.3f}'
ax.text(x_pos, mean + 30, mean_text, ha='center', va='bottom', color=color, fontsize=10, fontweight='bold')
# Set the Seaborn context
sns.set_context("notebook", font_scale=1)
# Create a dummy column
combined_data['dummy'] = 'All' # Add a dummy column for overall plotting
# Calculate median ARE scores for upregulated and downregulated genes for each tissue
median_scores = combined_data.groupby(['Tissue', 'Regulation'])['ARE_score'].median().unstack()
median_scores['Difference'] = median_scores['Upregulated'] - median_scores['Downregulated']
# Sort tissues based on the difference in median ARE scores
sorted_tissues = median_scores.sort_values(by='Difference', ascending=False).index
# Create an ordered categorical type to sort tissues in the plot
combined_data['Tissue'] = pd.Categorical(combined_data['Tissue'], categories=sorted_tissues, ordered=True)
# Create a figure with two subplots
fig, axes = plt.subplots(nrows=2, ncols=1, figsize=(8.5, 11))
# Plot for overall ARE score violin plot comparing up and downregulated genes across all tissues
sns.violinplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', split=True,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
sns.stripplot(data=combined_data, x='dummy', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[0])
axes[0].set_title('ARE Scores Across Tissues by Regulation Type')
axes[0].set_ylabel('ARE Score')
axes[0].set_xlabel("Regulation Type")
axes[0].set_xticklabels(["All Regulated Coding 3' UTR Sequences"])
# Add means annotation for overall plot
overall_up_mean = up_scores.mean()
overall_down_mean = down_scores.mean()
add_mean_annotation(axes[0], -0.2, overall_up_mean, 'orange')
add_mean_annotation(axes[0], 0.2, overall_down_mean, 'blue')
# Add p-value annotation for overall plot
add_p_val_annotation(axes[0], x1=-0.2, x2=0.2, y=max(combined_data['ARE_score']), p_val=p_value, h=max(combined_data['ARE_score']) * 0.1)
# Plot for ARE score distributions for each tissue, now ordered
violinplot = sns.violinplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', split=True,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
sns.stripplot(data=combined_data, x='Tissue', y='ARE_score', hue='Regulation', dodge=True, jitter=True, alpha=0.6,
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=axes[1])
axes[1].set_ylabel('ARE Score')
axes[1].set_xlabel('Tissue')
axes[0].get_legend().remove()
axes[1].set_title("ARE Scores by Tissue")
# Move Legend Outside
handles, labels = axes[1].get_legend_handles_labels()
axes[1].legend(handles[:2], labels[:2], loc='upper left', title='Regulation')
# Calculate and plot p-values for each tissue in the second subplot
positions = violinplot.get_xticks()
position_map = {group: pos for group, pos in zip(sorted_tissues, positions)}
grouped = combined_data.groupby('Tissue')
for tissue, group in grouped:
wt_scores = group[group['Regulation'] == 'Upregulated']['ARE_score']
ko_scores = group[group['Regulation'] == 'Downregulated']['ARE_score']
ks_stat, p_value = ks_2samp(wt_scores, ko_scores)
if not wt_scores.empty and not ko_scores.empty:
wt_pos = position_map[tissue] - 0.2 # Adjust depending on position
ko_pos = position_map[tissue] + 0.2 # Adjust depending on position
max_height = max(group['ARE_score'])
add_p_val_annotation(axes[1], wt_pos, ko_pos, max_height, p_value, h=max_height * 0.1)
plt.tight_layout()
# Save the figure as a .svg file
plt.savefig("Overall_ARE_distribution.svg", format="svg")
plt.show()
Series([], Name: Regulation, dtype: int64) Lowest ARE score in upregulated set: 1.0 Lowest ARE score in downregulated set: 1.0
/tmp/ipykernel_2823251/429978461.py:96: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
axes[0].set_xticklabels(["All Regulated Coding 3' UTR Sequences"])
/tmp/ipykernel_2823251/429978461.py:124: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
grouped = combined_data.groupby('Tissue')
In [23]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import os
from scipy.stats import ttest_ind
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Exclude genes found in Lung and Kidney tissues
excluded_tissues = ['Lung', 'Kidney']
combined_data = combined_data[~combined_data['Tissue'].isin(excluded_tissues)]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Checking for any gene appearing as both upregulated and downregulated in the same tissue
conflicting_genes = combined_data.groupby(['ID', 'Tissue'])['Regulation'].nunique()
conflicts = conflicting_genes[conflicting_genes > 1]
print(conflicts) # This should be empty if the data is clean and the duplicates are correctly removed
# Categorize the ARE scores into different categories
def categorize_scores(scores):
categories = pd.cut(scores, bins=[-1, 0, 4, float('inf')], labels=['ARE Score = 0', '0 < ARE Score ≤ 4', 'ARE Score > 4'])
return categories.value_counts()
# Proportion for upregulated genes
up_scores = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score']
up_categories = categorize_scores(up_scores)
# Proportion for downregulated genes
down_scores = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
down_categories = categorize_scores(down_scores)
# Set the Seaborn context
sns.set_context("notebook", font_scale=1.25)
# Function to plot pie charts and save them
def plot_and_save_pie_chart(categories, colors, filename, startangle=90, explode=(0, 0, 0.1)):
fig, ax = plt.subplots()
def autopct_func(pct, allvals):
absolute = int(round(pct / 100. * sum(allvals)))
return "{:.1f}%\n({:d})".format(pct, absolute)
wedges, texts, autotexts = ax.pie(
categories.values,
labels=categories.index,
colors=colors,
autopct=lambda pct: autopct_func(pct, categories.values),
startangle=startangle,
textprops=dict(color="black"),
explode=explode,
wedgeprops={"edgecolor": "black", "linewidth": 1}
)
for text in autotexts:
# Move the text higher so it fits better
text.set_position((text.get_position()[0], text.get_position()[1] - 0.2))
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.savefig(filename, format='svg')
plt.show() # Close the figure to prevent it from displaying immediately and to free memory
# Set a common start angle for consistency
start_angle = 90
# Explode the 'ARE Score = 0' slice larger than the other slices
explode_factor = (0.1, 0, 0)
# Plot and save upregulated pie chart with a gradient from light orange to dark orange
up_colors = ['orange', 'darkorange', 'orangered']
plot_and_save_pie_chart(up_categories, up_colors, 'upregulated_overall_pie.svg', startangle=start_angle, explode=explode_factor)
# Plot and save downregulated pie chart with a gradient from light blue to dark blue
down_colors = ['lightblue', 'blue', 'darkblue']
plot_and_save_pie_chart(down_categories, down_colors, 'downregulated_overall_pie.svg', startangle=start_angle, explode=explode_factor)
Series([], Name: Regulation, dtype: int64)
Histogram of ARE score count per ARE score for up and downregulated genes this should show us definitively that we have very few genes with high AREscores in the downreuglated set compared to the upregulated set
In [24]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Calculate the total number of genes
total_genes = len(combined_data)
# Define bins with a consistent width
binwidth = 1
bins = np.arange(-0.5, max(combined_data['ARE_score']) + binwidth + 0.5, binwidth)
# Plotting the histogram
plt.figure(figsize=(12, 6))
# Normalize the bin counts
upregulated_counts, _ = np.histogram(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'], bins=bins)
downregulated_counts, _ = np.histogram(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'], bins=bins)
# Convert counts to proportions
upregulated_norm = upregulated_counts / total_genes
downregulated_norm = downregulated_counts / total_genes
print(np.sum(upregulated_norm))
print(np.sum(downregulated_norm))
# Upregulated histogram
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'], bins=bins, kde=True, color='orange', label='Upregulated', alpha=0.5, stat='density')
# Downregulated histogram
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'], bins=bins, kde=True, color='blue', label='Downregulated', alpha=0.5, stat='density')
# Labels and Legend
plt.xlabel('ARE Score')
plt.ylabel('Proportion')
# Correctly set the ticks and labels
bin_labels = ['0'] + [f'{int(x)}' for x in np.arange(1, max(combined_data['ARE_score']) + 1)]
ticks = np.arange(0, max(combined_data['ARE_score']) + 1)
plt.xticks(ticks=ticks, labels=bin_labels, rotation=45, ha="center")
plt.title('Distribution of ARE Scores for Upregulated and Downregulated Genes (Normalized)')
plt.legend(title='Regulation')
# Save the figure
plt.savefig("ARE_score_histogram_binned_centered_normalized.svg", format="svg")
# Show the plot
plt.show()
0.5993555316863587 0.40064446831364126
In [25]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Cap the ARE scores at 20 for any values above 20
combined_data['ARE_score_capped'] = np.where(combined_data['ARE_score'] > 20, 20, combined_data['ARE_score'])
# Define bins as specified, with an additional bin edge for scores above 20
bins = np.concatenate([np.array([0]), np.arange(1, 5.1, 0.1), np.arange(5.1, 10.1, 0.1), np.arange(11, 21, 1), np.array([21])])
# Plotting the histogram
plt.figure(figsize=(12, 6))
# Upregulated histogram
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', label='Upregulated', alpha=0.7, stat='probability')
# Downregulated histogram
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', label='Downregulated', alpha=0.7, stat='probability')
# Labels and Legend
plt.xlabel('ARE Score')
plt.ylabel('Proportion')
# Correctly set the ticks and labels
major_ticks = np.concatenate([np.array([0]), np.arange(1, 6), np.arange(6, 11, 1), np.arange(11, 21, 1)])
major_labels = [str(x) if x != 21 else '20+' for x in major_ticks]
plt.xticks(ticks=major_ticks, labels=major_labels, rotation=45, ha="right")
# Title and Legend
plt.title('Distribution of ARE Scores for Upregulated and Downregulated Genes (Normalized)')
plt.legend(title='Regulation')
# Save the figure
plt.savefig("ARE_score_histogram_custom_bins_normalized.svg", format="svg")
# Show the plot
plt.show()
In [26]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Cap the ARE scores at 20 for any values above 20
combined_data['ARE_score_capped'] = np.where(combined_data['ARE_score'] > 20, 20, combined_data['ARE_score'])
# Define the bins for each subplot with a step size of 0.1
bins = np.arange(0, 21.1, 0.1) # Including from 0 to 21 to cover up to 20+
# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
# 0-6
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[0, 0], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[0, 0], stat='probability', alpha=0.7, label='Downregulated')
axes[0, 0].set_xlim(0, 6)
axes[0, 0].set_title('ARE Scores 0-6')
axes[0, 0].legend(loc='upper right')
# 5-11
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[0, 1], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[0, 1], stat='probability', alpha=0.7, label='Downregulated')
axes[0, 1].set_xlim(5, 11)
axes[0, 1].set_ylim(0,0.05)
axes[0, 1].set_title('ARE Scores 5-11')
axes[0, 1].legend(loc='upper right')
# 10-16
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[1, 0], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[1, 0], stat='probability', alpha=0.7, label='Downregulated')
axes[1, 0].set_xlim(10, 16)
axes[1, 0].set_ylim(0,0.05)
axes[1, 0].set_title('ARE Scores 10-16')
axes[1, 0].legend(loc='upper right')
# 15-20+
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[1, 1], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[1, 1], stat='probability', alpha=0.7, label='Downregulated')
axes[1, 1].set_xlim(15, 20.1)
axes[1, 1].set_ylim(0,0.05)
axes[1, 1].set_title('ARE Scores 15-20+')
axes[1, 1].set_xticks([15, 16, 17, 18, 19, 20])
axes[1, 1].set_xticklabels(['15', '16', '17', '18', '19', '20+'])
axes[1, 1].legend(loc='upper right')
# Set common labels
for ax in axes.flat:
ax.set_ylabel('Proportion')
# Adjust layout and display
fig.tight_layout()
plt.savefig("ARE_score_histogram_custom_subplots_normalized.svg", format="svg")
plt.show()
In [27]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Cap the ARE scores at 20 for any values above 20
combined_data['ARE_score_capped'] = np.where(combined_data['ARE_score'] > 20, 20, combined_data['ARE_score'])
# Define bins for the histogram
bins = np.arange(0, 21.1, 0.1) # Including from 0 to 21 to cover up to 20+
# Plotting the histogram
plt.figure(figsize=(12, 6))
# Upregulated histogram
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', label='Upregulated', alpha=0.7, stat='probability')
# Downregulated histogram
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', label='Downregulated', alpha=0.7, stat='probability')
# Labels and Legend
plt.xlabel('ARE Score')
plt.ylabel('Proportion')
plt.title('Distribution of ARE Scores for Upregulated and Downregulated Genes (Normalized)')
# Define major ticks and labels
major_ticks = np.arange(0, 21, 1)
major_labels = [str(x) if x != 20 else '20+' for x in major_ticks]
# Adjust tick positions and labels
plt.xticks(ticks=major_ticks + 0.05, labels=major_labels, rotation=45, ha="right")
# Title and Legend
plt.legend(title='Regulation')
# Save the figure
plt.savefig("ARE_score_histogram_custom_bins_normalized.svg", format="svg")
# Show the plot
plt.show()
Redone with no redundant bins
In [28]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Cap the ARE scores at 20 for any values above 20
combined_data['ARE_score_capped'] = np.where(combined_data['ARE_score'] > 20, 20, combined_data['ARE_score'])
# Define the bins for each subplot with a step size of 0.1
bins = np.arange(0, 21.1, 0.1) # Including from 0 to 21 to cover up to 20+
# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
# 0-5
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[0, 0], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[0, 0], stat='probability', alpha=0.7, label='Downregulated')
axes[0, 0].set_xlim(0, 5)
axes[0, 0].set_title('ARE Scores 0-5')
axes[0, 0].legend(loc='upper right')
# 5-10
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[0, 1], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[0, 1], stat='probability', alpha=0.7, label='Downregulated')
axes[0, 1].set_xlim(5, 10)
axes[0, 1].set_title('ARE Scores 5-10')
axes[0, 1].legend(loc='upper right')
# 10-15
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[1, 0], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[1, 0], stat='probability', alpha=0.7, label='Downregulated')
axes[1, 0].set_xlim(10, 15)
axes[1, 0].set_title('ARE Scores 10-15')
axes[1, 0].legend(loc='upper right')
# 15-20+
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[1, 1], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[1, 1], stat='probability', alpha=0.7, label='Downregulated')
axes[1, 1].set_xlim(15, 20.1)
axes[1, 1].set_title('ARE Scores 15-20+')
axes[1, 1].set_xticks([15, 16, 17, 18, 19, 20])
axes[1, 1].set_xticklabels(['15', '16', '17', '18', '19', '20+'])
axes[1, 1].legend(loc='upper right')
# Set common labels
for ax in axes.flat:
ax.set_ylabel('Proportion')
# Adjust layout and display
fig.tight_layout()
plt.savefig("ARE_score_histogram_custom_subplots_normalized.svg", format="svg")
plt.show()
In [29]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Cap the ARE scores at 20 for any values above 20
combined_data['ARE_score_capped'] = np.where(combined_data['ARE_score'] > 20, 20, combined_data['ARE_score'])
# Define the bins for the histogram with a step size of 0.1
bins = np.arange(0, 21.1, 0.1)
# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
def annotate_bars(ax):
for p in ax.patches:
height = p.get_height()
width = p.get_x() + p.get_width() / 2
bin_value = round(p.get_x(), 1) # Annotate the left edge of each bin
if height > 0:
ax.annotate(f'{bin_value}', (width, height), ha='center', va='bottom', fontsize=8, rotation=90)
# 0-5
ax = axes[0, 0]
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=ax, stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=ax, stat='probability', alpha=0.7, label='Downregulated')
ax.set_xlim(0, 5)
ax.set_xticks([0, 1, 2, 3, 4, 5])
ax.set_title('ARE Scores 0-5')
ax.legend(loc='upper right')
# annotate_bars(ax)
# 5-10
ax = axes[0, 1]
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=ax, stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=ax, stat='probability', alpha=0.7, label='Downregulated')
ax.set_xlim(5, 10)
ax.set_xticks([5, 6, 7, 8, 9, 10])
ax.set_title('ARE Scores 5-10')
ax.legend(loc='upper right')
# annotate_bars(ax)
# 10-15
ax = axes[1, 0]
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=ax, stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=ax, stat='probability', alpha=0.7, label='Downregulated')
ax.set_xlim(10, 15)
ax.set_xticks([10, 11, 12, 13, 14, 15])
ax.set_title('ARE Scores 10-15')
ax.legend(loc='upper right')
# annotate_bars(ax)
# 15-20+
ax = axes[1, 1]
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=ax, stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=ax, stat='probability', alpha=0.7, label='Downregulated')
ax.set_xlim(15, 20.1)
ax.set_xticks([15, 16, 17, 18, 19, 20])
ax.set_xticklabels(['15', '16', '17', '18', '19', '20+'])
ax.set_title('ARE Scores 15-20+')
ax.legend(loc='upper right')
# annotate_bars(ax)
# Set common labels
for ax in axes.flat:
ax.set_ylabel('Probability')
# Adjust layout and display
fig.tight_layout()
plt.savefig("ARE_score_histogram_custom_subplots_annotated.svg", format="svg")
plt.show()
In [30]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Cap the ARE scores at 20 for any values above 20
combined_data['ARE_score_capped'] = np.where(combined_data['ARE_score'] > 20, 20, combined_data['ARE_score'])
# Define the bins for each subplot with a step size of 0.1
bins = np.arange(0, 21.1, 0.1) # Including from 0 to 21 to cover up to 20+
# First, calculate the overall max proportion for setting consistent y-axis limits
hist_data = sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, stat='probability', alpha=0.7, label='Upregulated')
hist_data = sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, stat='probability', alpha=0.7, label='Downregulated')
max_prop = hist_data.get_ylim()[1]
plt.close()
# Create subplots
fig, axes = plt.subplots(2, 2, figsize=(15, 10))
# 0-5
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[0, 0], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[0, 0], stat='probability', alpha=0.7, label='Downregulated')
axes[0, 0].set_xlim(0, 5)
axes[0, 0].set_ylim(0, max_prop)
axes[0, 0].set_title('ARE Scores 0-5')
axes[0, 0].legend(loc='upper right')
# 6-10
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[0, 1], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[0, 1], stat='probability', alpha=0.7, label='Downregulated')
axes[0, 1].set_xlim(6, 10)
axes[0, 1].set_ylim(0, max_prop)
axes[0, 1].set_title('ARE Scores 6-10')
axes[0, 1].legend(loc='upper right')
# 11-15
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[1, 0], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[1, 0], stat='probability', alpha=0.7, label='Downregulated')
axes[1, 0].set_xlim(11, 15)
axes[1, 0].set_ylim(0, max_prop)
axes[1, 0].set_title('ARE Scores 11-15')
axes[1, 0].legend(loc='upper right')
# 16-20+
sns.histplot(combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score_capped'], bins=bins, kde=False, color='orange', ax=axes[1, 1], stat='probability', alpha=0.7, label='Upregulated')
sns.histplot(combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score_capped'], bins=bins, kde=False, color='blue', ax=axes[1, 1], stat='probability', alpha=0.7, label='Downregulated')
axes[1, 1].set_xlim(16, 20.1)
axes[1, 1].set_ylim(0, max_prop)
axes[1, 1].set_title('ARE Scores 16-20+')
axes[1, 1].set_xticks([16, 17, 18, 19, 20])
axes[1, 1].set_xticklabels(['16', '17', '18', '19', '20+'])
axes[1, 1].legend(loc='upper right')
# Set common labels
for ax in axes.flat:
ax.set_ylabel('Proportion')
# Adjust layout and display
fig.tight_layout()
plt.savefig("ARE_score_histogram_custom_subplots_normalized.svg", format="svg")
plt.show()
In [31]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score'])
combined_data = combined_data[combined_data['ARE_score'] >= 0]
# Check for any ARE scores above 20
scores_above_20 = combined_data[combined_data['ARE_score'] > 20]
# Print the number of scores above 20
print(f"Number of ARE scores above 20: {len(scores_above_20)}")
Number of ARE scores above 20: 49
Here is ARE-score now plotted against log2fc as a simple volcano plot:
In [32]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Plotting the volcano plot
plt.figure(figsize=(12, 8))
# Volcano plot
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation', alpha=0.7, palette={'Upregulated': 'orange', 'Downregulated': 'blue'})
# Labels and Title
plt.xlabel('log2FoldChange')
plt.ylabel('ARE Score')
plt.title('Volcano Plot of ARE Scores vs log2FoldChange')
# Adding horizontal and vertical lines to separate upregulated and downregulated genes if required
plt.axvline(x=0, linestyle='--', color='black', linewidth=1)
# Show Legend
plt.legend(title='Regulation')
# Save the figure
plt.savefig("ARE_score_vs_log2FoldChange_volcano_plot.svg", format="svg")
# Show the plot
plt.show()
Here I add some marginal histograms for both variables as another way to associate these variables and their trends between them
In [33]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Setting up the figure with marginal histograms using JointGrid
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
# Add scatterplot
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_joint, alpha=0.6)
# Plot trend lines
sns.regplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='orange', label='Upregulated Trend')
sns.regplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='blue', label='Downregulated Trend')
# Calculate and annotate Pearson correlation coefficient
pearson_corr_up, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'])
pearson_corr_down, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'])
# Add annotations for Pearson correlation
g.ax_joint.text(0.05, 0.95, f'Upreg. Pearson r: {pearson_corr_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.90, f'Downreg. Pearson r: {pearson_corr_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add titles and labels
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Marginal Histograms and Trend Lines', y=1.03)
# Customize marginal distributions for better visibility, separate by 'Regulation'
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6)
# Add legend for trend lines
g.ax_joint.legend(loc='upper left')
# Save the figure
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_annotated_improved.svg", format="svg")
# Show the plot
plt.show()
Same thing with mean and median annotations added and legends so that they do not overlap one another
In [34]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Setting up the figure with marginal histograms using JointGrid
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
# Add scatterplot without legend
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_joint, alpha=0.6, legend=False)
# Plot trend lines
up_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='orange', label='Upregulated Trend')
down_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='blue', label='Downregulated Trend')
# Calculate and annotate Pearson correlation coefficient
pearson_corr_up, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'])
pearson_corr_down, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'])
# Add annotations for Pearson correlation
g.ax_joint.text(0.05, 0.95, f'Upreg. Pearson r: {pearson_corr_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.90, f'Downreg. Pearson r: {pearson_corr_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add horizontal lines to indicate threshold in ARE scores
g.ax_joint.axhline(y=0, color='grey', linestyle='--', linewidth=1)
# Add titles and labels
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Marginal Histograms and Trend Lines', y=1.03)
# Customize marginal distributions for better visibility, separate by 'Regulation'
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6)
# Add density plots on top of the histograms to show the distribution more clearly
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', ax=g.ax_marg_x, color='orange', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', ax=g.ax_marg_x, color='blue', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], y='ARE_score', ax=g.ax_marg_y, color='orange', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], y='ARE_score', ax=g.ax_marg_y, color='blue', fill=True, alpha=0.2)
# Add summary statistics as text annotations
mean_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
median_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].median()
median_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].median()
g.ax_joint.text(0.65, 0.85, f'Upreg. ARE Mean: {mean_up:.2f}\nUpreg. ARE Median: {median_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.65, 0.75, f'Downreg. ARE Mean: {mean_down:.2f}\nDownreg. ARE Median: {median_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add a single custom legend
handles, labels = g.ax_joint.get_legend_handles_labels()
g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
# Save the figure
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_annotated_improved_further.svg", format="svg")
# Show the plot
plt.show()
/tmp/ipykernel_2823251/2894176590.py:80: UserWarning: Mismatched number of handles and labels: len(handles) = 2 len(labels) = 4 g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
Here is the same plot with slope annotations added
In [35]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Setting up the figure with marginal histograms using JointGrid
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
# Add scatterplot without legend
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_joint, alpha=0.6, legend=False)
# Plot trend lines
up_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='orange', label='Upregulated Trend')
down_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='blue', label='Downregulated Trend')
# Calculate and annotate Pearson correlation coefficient
pearson_corr_up, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'])
pearson_corr_down, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'])
# Calculate slopes for trend lines
slope_up = np.polyfit(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'], 1)[0]
slope_down = np.polyfit(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'], 1)[0]
# Add annotations for Pearson correlation and slopes
g.ax_joint.text(0.05, 0.95, f'Upreg. Pearson r: {pearson_corr_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.90, f'Downreg. Pearson r: {pearson_corr_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.85, f'Upreg. Slope: {slope_up:.3f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.80, f'Downreg. Slope: {slope_down:.3f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add horizontal lines to indicate threshold in ARE scores
g.ax_joint.axhline(y=0, color='grey', linestyle='--', linewidth=1)
# Add titles and labels
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Marginal Histograms and Trend Lines', y=1.03)
# Customize marginal distributions for better visibility, separate by 'Regulation'
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6)
# Add density plots on top of the histograms to show the distribution more clearly
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', ax=g.ax_marg_x, color='orange', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', ax=g.ax_marg_x, color='blue', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], y='ARE_score', ax=g.ax_marg_y, color='orange', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], y='ARE_score', ax=g.ax_marg_y, color='blue', fill=True, alpha=0.2)
# Add summary statistics as text annotations
mean_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
median_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].median()
median_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].median()
g.ax_joint.text(0.65, 0.85, f'Upreg. ARE Mean: {mean_up:.2f}\nUpreg. ARE Median: {median_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.65, 0.75, f'Downreg. ARE Mean: {mean_down:.2f}\nDownreg. ARE Median: {median_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add a single custom legend
handles, labels = g.ax_joint.get_legend_handles_labels()
g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
# Save the figure
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_annotated_slopes.svg", format="svg")
# Show the plot
plt.show()
/tmp/ipykernel_2823251/2272620246.py:86: UserWarning: Mismatched number of handles and labels: len(handles) = 2 len(labels) = 4 g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
Same plot but this time I use density hexgram
In [36]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Setting up the figure with marginal histograms using JointGrid
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
# Add hexbin plot to show density
hb = g.ax_joint.hexbin(combined_data['log2FoldChange'], combined_data['ARE_score'], gridsize=50, cmap='Blues', mincnt=1)
# Add color bar for the hexbin plot
cb = g.fig.colorbar(hb, ax=g.ax_joint, orientation='vertical')
cb.set_label('Counts')
# Plot trend lines
up_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='orange', label='Upregulated Trend')
down_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='blue', label='Downregulated Trend')
# Calculate and annotate Pearson correlation coefficient
pearson_corr_up, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'])
pearson_corr_down, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'])
# Calculate slopes for trend lines
slope_up = np.polyfit(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'], 1)[0]
slope_down = np.polyfit(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'], 1)[0]
# Add annotations for Pearson correlation and slopes
g.ax_joint.text(0.05, 0.95, f'Upreg. Pearson r: {pearson_corr_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.90, f'Downreg. Pearson r: {pearson_corr_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.85, f'Upreg. Slope: {slope_up:.3f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.80, f'Downreg. Slope: {slope_down:.3f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add horizontal lines to indicate threshold in ARE scores
g.ax_joint.axhline(y=0, color='grey', linestyle='--', linewidth=1)
# Add titles and labels
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Hexbin Density and Trend Lines', y=1.03)
# Customize marginal distributions for better visibility, separate by 'Regulation'
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6)
# Add summary statistics as text annotations
mean_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
median_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].median()
median_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].median()
g.ax_joint.text(0.65, 0.85, f'Upreg. ARE Mean: {mean_up:.2f}\nUpreg. ARE Median: {median_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.65, 0.75, f'Downreg. ARE Mean: {mean_down:.2f}\nDownreg. ARE Median: {median_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add a single custom legend
handles, labels = g.ax_joint.get_legend_handles_labels()
g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
# Save the figure
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_annotated_density.svg", format="svg")
# Show the plot
plt.show()
/tmp/ipykernel_2823251/1615529175.py:84: UserWarning: Mismatched number of handles and labels: len(handles) = 2 len(labels) = 4 g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
Now here is an iteration of this same plot just this time all genes must have AREscore>0 and a log2FC absolute value of 2 or more these can be modified to watch the distribution change
In [37]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Loading the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combining the data and excluding rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Filtering to include only genes with ARE scores greater than 0 and absolute value of log2FoldChange greater than 2
combined_data = combined_data[(combined_data['ARE_score'] > 0) & (combined_data['log2FoldChange'].abs() > 4)]
# Dropping duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Setting up the figure with marginal histograms using JointGrid
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
# Add scatterplot without legend
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_joint, alpha=0.6, legend=False)
# Plot trend lines
up_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='orange', label='Upregulated Trend')
down_trend = sns.regplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, color='blue', label='Downregulated Trend')
# Calculate and annotate Pearson correlation coefficient
pearson_corr_up, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'])
pearson_corr_down, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'])
# Calculate slopes for trend lines
slope_up = np.polyfit(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'], 1)[0]
slope_down = np.polyfit(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'], 1)[0]
# Add annotations for Pearson correlation and slopes
g.ax_joint.text(0.05, 0.95, f'Upreg. Pearson r: {pearson_corr_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.90, f'Downreg. Pearson r: {pearson_corr_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.85, f'Upreg. Slope: {slope_up:.3f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.05, 0.80, f'Downreg. Slope: {slope_down:.3f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add horizontal lines to indicate threshold in ARE scores
g.ax_joint.axhline(y=0, color='grey', linestyle='--', linewidth=1)
# Add titles and labels
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Marginal Histograms and Trend Lines', y=1.03)
# Customize marginal distributions for better visibility, separate by 'Regulation'
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6)
# Add density plots on top of the histograms to show the distribution more clearly
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', ax=g.ax_marg_x, color='orange', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', ax=g.ax_marg_x, color='blue', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], y='ARE_score', ax=g.ax_marg_y, color='orange', fill=True, alpha=0.2)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], y='ARE_score', ax=g.ax_marg_y, color='blue', fill=True, alpha=0.2)
# Add summary statistics as text annotations
mean_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
median_up = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].median()
median_down = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].median()
g.ax_joint.text(0.65, 0.85, f'Upreg. ARE Mean: {mean_up:.2f}\nUpreg. ARE Median: {median_up:.2f}', transform=g.ax_joint.transAxes, color='orange', fontsize=12, verticalalignment='top')
g.ax_joint.text(0.65, 0.75, f'Downreg. ARE Mean: {mean_down:.2f}\nDownreg. ARE Median: {median_down:.2f}', transform=g.ax_joint.transAxes, color='blue', fontsize=12, verticalalignment='top')
# Add a single custom legend
handles, labels = g.ax_joint.get_legend_handles_labels()
g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
# Save the figure
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_annotated_filtered_log2fc.svg", format="svg")
# Show the plot
plt.show()
/tmp/ipykernel_2823251/236085898.py:89: UserWarning: Mismatched number of handles and labels: len(handles) = 2 len(labels) = 4 g.ax_joint.legend(handles=handles, labels=["Upregulated", "Downregulated", "Upregulated Trend", "Downregulated Trend"], loc="upper right", frameon=True)
Now I do this same plot as a hexbin plot to see if it does this better
In [38]:
plt.figure(figsize=(12, 8))
plt.hexbin(combined_data['log2FoldChange'], combined_data['ARE_score'], gridsize=50, cmap='viridis', extent=(-10, 10, 0, 60))
cb = plt.colorbar(label='Count')
plt.title('Hexbin plot of ARE Scores vs log2FoldChange')
plt.xlabel('log2FoldChange')
plt.ylabel('ARE Score')
plt.savefig('hexbin_plot_ARE_score_vs_log2FoldChange.svg', format='svg')
plt.show()
Here is an optimized hexbin plot
In [39]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Load the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and exclude rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Drop duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Create hexbin plot
plt.figure(figsize=(14, 10))
hb = plt.hexbin(combined_data['log2FoldChange'], combined_data['ARE_score'], gridsize=70, cmap='viridis', extent=(-10, 10, 0, 50), mincnt=1)
cb = plt.colorbar(label='Count')
cb.set_label('Count', fontsize=14)
# Add mean ARE scores for upregulated and downregulated
mean_upreg_are = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_downreg_are = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
plt.axvline(0, color='black', linestyle='--', linewidth=2)
plt.text(4, 45, f'Upreg. Mean ARE: {mean_upreg_are:.2f}', color='orange', fontsize=14, bbox=dict(facecolor='white', alpha=0.7))
plt.text(-9, 45, f'Downreg. Mean ARE: {mean_downreg_are:.2f}', color='blue', fontsize=14, bbox=dict(facecolor='white', alpha=0.7))
# Annotate dense regions
densest_bin = hb.get_offsets()[np.argmax(hb.get_array())]
plt.annotate('Most Dense Region', xy=densest_bin, xytext=(densest_bin[0], densest_bin[1]+5),
arrowprops=dict(facecolor='white', shrink=0.05), color='white', fontsize=12, ha='center', bbox=dict(facecolor='black', alpha=0.5))
# Add titles and labels
plt.title('Enhanced Hexbin Plot of ARE Scores vs log2FoldChange', fontsize=18, pad=20)
plt.xlabel('log2FoldChange', fontsize=14)
plt.ylabel('ARE Score', fontsize=14)
# Add grid lines and set axis limits
plt.grid(True, which='both', linestyle='--', linewidth=0.7, alpha=0.7)
plt.xlim(-10, 10)
plt.ylim(0, 50)
# Save the figure
plt.savefig('enhanced_hexbin_plot_ARE_score_vs_log2FoldChange_fullrange.svg', format='svg')
# Show the plot
plt.show()
Here is the scatter plot but instead of lines of best fit here I use density annotations
In [40]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr
# Function to load data from CSV files
def load_data(directory, regulation_type):
all_data = []
for filename in os.listdir(directory):
if filename.endswith('_updated.csv'):
file_path = os.path.join(directory, filename)
data = pd.read_csv(file_path)
data['Regulation'] = regulation_type
data['Tissue'] = filename.split('_')[0] # Assuming the filename contains the tissue name at the start
all_data.append(data)
return pd.concat(all_data, ignore_index=True)
# Define the paths to your data directories, adjust these paths to your actual dataset paths
upregulated_path = './upregulated_genes_updated'
downregulated_path = './downregulated_genes_updated'
# Load the upregulated and downregulated data
upregulated_data = load_data(upregulated_path, 'Upregulated')
downregulated_data = load_data(downregulated_path, 'Downregulated')
# Combine the data and exclude rows with Score = 0 or missing
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Drop duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Set up the figure with marginal histograms using JointGrid
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
# Add scatterplot with reduced alpha to make points more distinct
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_joint, alpha=0.3)
# Add density plot overlay
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score', ax=g.ax_joint, color='orange', alpha=0.5, linewidths=1.5)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score', ax=g.ax_joint, color='blue', alpha=0.5, linewidths=1.5)
# Calculate and annotate Pearson correlation coefficient
pearson_corr_up, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Upregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'])
pearson_corr_down, _ = pearsonr(combined_data[combined_data['Regulation'] == 'Downregulated']['log2FoldChange'], combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'])
# Add annotations for Pearson correlation
# Adjust positions to prevent overlap
g.ax_joint.text(-9.5, 50, f'Upreg. Pearson r: {pearson_corr_up:.2f}', color='orange', fontsize=12)
g.ax_joint.text(-9.5, 45, f'Downreg. Pearson r: {pearson_corr_down:.2f}', color='blue', fontsize=12)
# Calculate mean ARE scores and annotate
mean_upreg_are = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_downreg_are = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
# Adjust positions to prevent overlap
g.ax_joint.text(4.5, 50, f'Upreg. Mean ARE: {mean_upreg_are:.2f}', color='orange', fontsize=12)
g.ax_joint.text(4.5, 45, f'Downreg. Mean ARE: {mean_downreg_are:.2f}', color='blue', fontsize=12)
# Add Upregulated and Downregulated labels
g.ax_joint.text(3, 40, 'Upregulated', fontsize=14, color='orange', weight='bold')
g.ax_joint.text(-9, 40, 'Downregulated', fontsize=14, color='blue', weight='bold')
# Add titles and labels
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Marginal Histograms, Trend Lines, and Density', y=1.03)
# Customize marginal distributions for better visibility, separate by 'Regulation'
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6, legend=False)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack', palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6, legend=False)
# Move joint plot and legend only for main plot
handles, labels = g.ax_joint.get_legend_handles_labels()
g.ax_joint.legend(handles=handles, labels=labels, loc='upper left', bbox_to_anchor=(1, 1.2), title='Regulation')
# Save the figure
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_with_density_improved.svg", format="svg")
# Show the plot
plt.show()
Density annotations with an overall line of best fit which kind of shows that there is an ARE score increase across up from downregulated
In [41]:
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import os
from scipy.stats import pearsonr, ttest_ind
# Load data - Your existing code here
# Calculate mean ARE scores and T-test p-value
mean_upreg_are = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_downreg_are = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
# T-test to compare ARE scores
t_stat, p_value = ttest_ind(
combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'],
combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score']
)
# Code for the plot
g = sns.JointGrid(data=combined_data, x='log2FoldChange', y='ARE_score', height=10)
sns.scatterplot(data=combined_data, x='log2FoldChange', y='ARE_score', hue='Regulation',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_joint, alpha=0.3)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Upregulated'], x='log2FoldChange', y='ARE_score',
ax=g.ax_joint, color='orange', alpha=0.5, linewidths=1.5)
sns.kdeplot(data=combined_data[combined_data['Regulation'] == 'Downregulated'], x='log2FoldChange', y='ARE_score',
ax=g.ax_joint, color='blue', alpha=0.5, linewidths=1.5)
sns.histplot(data=combined_data, x='log2FoldChange', hue='Regulation', multiple='stack',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_x, bins=30, alpha=0.6, legend=False)
sns.histplot(data=combined_data, y='ARE_score', hue='Regulation', multiple='stack',
palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, ax=g.ax_marg_y, bins=30, alpha=0.6, legend=False)
g.ax_joint.text(4.5, 50, f'Upreg. Mean ARE: {mean_upreg_are:.2f}', color='orange', fontsize=12)
g.ax_joint.text(4.5, 45, f'Downreg. Mean ARE: {mean_downreg_are:.2f}', color='blue', fontsize=12)
# Add t-test p-value
g.ax_joint.text(4.5, 40, f'T-test p-value: {p_value:.2e}', color='green', fontsize=12)
sns.regplot(data=combined_data, x='log2FoldChange', y='ARE_score', scatter=False, ax=g.ax_joint, line_kws={"color":"red"})
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Joint Plot of ARE Scores vs log2FoldChange with Marginal Histograms, Regression Line, and Density', y=1.03)
handles, labels = g.ax_joint.get_legend_handles_labels()
g.ax_joint.legend(handles=handles, labels=labels, loc='upper left', bbox_to_anchor=(1, 1.2), title='Regulation')
g.savefig("ARE_score_vs_log2FoldChange_joint_plot_with_density_regression_ttest.svg", format="svg")
plt.show()
# Boxplot comparison
plt.figure(figsize=(10, 6))
sns.boxplot(data=combined_data, x='Regulation', y='ARE_score', palette={'Upregulated': 'orange', 'Downregulated': 'blue'})
plt.title('Boxplot of ARE Scores for Upregulated and Downregulated Genes')
plt.savefig("ARE_score_boxplot.svg", format="svg")
plt.show()
/tmp/ipykernel_2823251/3130667382.py:57: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(data=combined_data, x='Regulation', y='ARE_score', palette={'Upregulated': 'orange', 'Downregulated': 'blue'})
This plot was made to highlight the tail ends of the distributions
In [42]:
g = sns.FacetGrid(combined_data, col="Regulation", palette={'Upregulated': 'orange', 'Downregulated': 'blue'}, height=6, aspect=1.2)
g.map(sns.scatterplot, "log2FoldChange", "ARE_score", alpha=0.3)
g.map(sns.lineplot, "log2FoldChange", "ARE_score", ci=None, estimator=np.median)
g.set_axis_labels("log2FoldChange", "ARE Score")
g.add_legend()
plt.subplots_adjust(top=0.8)
g.fig.suptitle('Scatter Plots of ARE Scores vs log2FoldChange for Upregulated and Downregulated Genes')
g.savefig("ARE_score_facet_scatter_plot.svg", format="svg")
plt.show()
/home/gsgeorge/.conda/envs/mus_musculus/lib/python3.12/site-packages/seaborn/axisgrid.py:854: FutureWarning: The `ci` parameter is deprecated. Use `errorbar=None` for the same effect. func(*plot_args, **plot_kwargs) /home/gsgeorge/.conda/envs/mus_musculus/lib/python3.12/site-packages/seaborn/axisgrid.py:854: FutureWarning: The `ci` parameter is deprecated. Use `errorbar=None` for the same effect. func(*plot_args, **plot_kwargs)
Better annotated version of the plot above:
In [43]:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# Sample data loading function here
# all_data = ... (This represents your loaded data)
# Combine the data and exclude rows with ARE_score = 0 or missing values
combined_data = pd.concat([upregulated_data, downregulated_data], ignore_index=True)
combined_data = combined_data.dropna(subset=['ARE_score', 'log2FoldChange'])
# Drop duplicate IDs within the same tissue for up and downregulated genes
combined_data = combined_data.drop_duplicates(subset=['ID', 'Tissue', 'Regulation'])
# Calculate mean ARE scores for annotation
mean_upreg_are = combined_data[combined_data['Regulation'] == 'Upregulated']['ARE_score'].mean()
mean_downreg_are = combined_data[combined_data['Regulation'] == 'Downregulated']['ARE_score'].mean()
# Setting up the FacetGrid
palette = {'Upregulated': 'orange', 'Downregulated': 'blue'}
g = sns.FacetGrid(combined_data, col="Regulation", palette=palette, height=6, aspect=1.2)
# Adding scatter plot
g.map(sns.scatterplot, "log2FoldChange", "ARE_score", alpha=0.3, edgecolor=None)
# Adding smooth trend lines
g.map(sns.regplot, "log2FoldChange", "ARE_score", scatter=False, ci=None, lowess=True, color='red')
# Adding mean ARE score annotations
def annotate(data, **kws):
reg = data['Regulation'].iloc[0]
ax = plt.gca()
if reg == 'Upregulated':
mean_are = mean_upreg_are
color = 'orange'
else:
mean_are = mean_downreg_are
color = 'blue'
ax.text(0.05, 0.95, f'Mean ARE: {mean_are:.2f}', transform=ax.transAxes, color=color, fontsize=12, verticalalignment='top')
g.map_dataframe(annotate)
# Adjusting the axes labels and title
g.set_axis_labels("log2FoldChange", "ARE Score")
g.fig.suptitle('Scatter Plots of ARE Scores vs log2FoldChange for Upregulated and Downregulated Genes', y=1.02)
# Adjusting layout for better appearance
plt.subplots_adjust(top=0.85, wspace=0.3)
# Save the figure
g.savefig("ARE_score_facet_scatter_plot_annotated.svg", format="svg")
# Show the plot
plt.show()
In [44]:
import os
import pandas as pd
# Function to search for a list of gene names within CSV files in directories
def find_genes_in_csv(directories, gene_names):
gene_files = {gene: [] for gene in gene_names}
for directory in directories:
for root, _, files in os.walk(directory):
csv_files = [f for f in files if f.endswith('.csv')]
for file in csv_files:
file_path = os.path.join(root, file)
try:
# Read the CSV file into a DataFrame
df = pd.read_csv(file_path)
# Check if Gene.name column exists
if 'Gene.name' in df.columns:
for gene in gene_names:
if gene in df['Gene.name'].values:
gene_files[gene].append(file_path)
except Exception as e:
print(f"Error reading {file}: {e}")
return gene_files
# List of directories to search
directories = [
'/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/upregulated_genes_updated',
'/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated',
'/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated'
]
# List of genes to search for
gene_names_of_interest = [
'Notch1',
'Cebpb', # CCAAT/enhancer binding protein-beta
'Junb',
'Il1b',
'Klf2',
'Zfp36',
'Zfp36l1',
'Itgb2'
]
# Find CSV files containing the gene names
gene_files = find_genes_in_csv(directories, gene_names_of_interest)
# Print results
for gene, files in gene_files.items():
print(f"\nCSV files containing the gene '{gene}':")
for file in files:
print(file)
CSV files containing the gene 'Notch1': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Liver_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/BM_unregulated_annotated_updated.csv CSV files containing the gene 'Cebpb': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated/BM_downregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Liver_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv CSV files containing the gene 'Junb': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/upregulated_genes_updated/Liver_upregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated/BM_downregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv CSV files containing the gene 'Il1b': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/upregulated_genes_updated/Liver_upregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated/BM_downregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv CSV files containing the gene 'Klf2': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Liver_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/BM_unregulated_annotated_updated.csv CSV files containing the gene 'Zfp36': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated/BM_downregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Liver_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv CSV files containing the gene 'Zfp36l1': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Liver_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/BM_unregulated_annotated_updated.csv CSV files containing the gene 'Itgb2': /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated/BM_downregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Ovary_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Lung_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Spleen_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Liver_unregulated_annotated_updated.csv /mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated/Kidney_unregulated_annotated_updated.csv
In [45]:
import os
import pandas as pd
# Function to search for a list of gene names within CSV files in directories and classify them
def find_genes_in_csv(directories, gene_names):
gene_files = {gene: {'Upregulated': [], 'Unregulated': [], 'Downregulated': []} for gene in gene_names}
for directory in directories:
category = 'Unregulated' # Default category
if 'upregulated' in directory.lower():
category = 'Upregulated'
elif 'downregulated' in directory.lower():
category = 'Downregulated'
for root, _, files in os.walk(directory):
csv_files = [f for f in files if f.endswith('.csv')]
for file in csv_files:
file_path = os.path.join(root, file)
try:
# Read the CSV file into a DataFrame
df = pd.read_csv(file_path)
# Check if Gene.name column exists
if 'Gene.name' in df.columns:
for gene in gene_names:
if gene in df['Gene.name'].values:
# Extract tissue name from the file name
tissue = file.replace('_annotated_updated.csv', '').split('_')[0]
gene_files[gene][category].append(tissue)
except Exception as e:
print(f"Error reading {file}: {e}")
return gene_files
# List of directories to search
directories = [
'/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/upregulated_genes_updated',
'/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/downregulated_genes_updated',
'/mnt/work_1/gsgeorge/mus_musculus/ARE_SCORE_ALL/unregulated_genes_updated'
]
# List of genes to search for
gene_names_of_interest = [
'Notch1',
'Cebpb', # CCAAT/enhancer binding protein-beta
'Junb',
'Il1b',
'Klf2',
'Zfp36',
'Zfp36l1',
'Itgb2'
]
# Find CSV files containing the gene names
gene_files = find_genes_in_csv(directories, gene_names_of_interest)
# Print results
for gene, categories in gene_files.items():
print(f"\n{gene}")
for category, tissues in categories.items():
print(f"{category}:")
print(f"[{', '.join(tissues)}]" if tissues else "[None]")
Notch1 Upregulated: [None] Unregulated: [Ovary, Lung, Spleen, Liver, Kidney, BM] Downregulated: [None] Cebpb Upregulated: [None] Unregulated: [Ovary, Lung, Spleen, Liver, Kidney] Downregulated: [BM] Junb Upregulated: [Liver] Unregulated: [Ovary, Lung, Spleen, Kidney] Downregulated: [BM] Il1b Upregulated: [Liver] Unregulated: [Lung, Spleen, Kidney] Downregulated: [BM] Klf2 Upregulated: [None] Unregulated: [Ovary, Lung, Spleen, Liver, Kidney, BM] Downregulated: [None] Zfp36 Upregulated: [None] Unregulated: [Ovary, Lung, Spleen, Liver, Kidney] Downregulated: [BM] Zfp36l1 Upregulated: [None] Unregulated: [Ovary, Lung, Spleen, Liver, Kidney, BM] Downregulated: [None] Itgb2 Upregulated: [None] Unregulated: [Ovary, Lung, Spleen, Liver, Kidney] Downregulated: [BM]